the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research, we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter
EXTRACTIVE
discover the trends of the topics and find relationship between LDA topics and paper features and generate trust tags,  learn a LDA model with 100 topics; $\alpha =0.01$, $\beta = 0.01$ and using Gibbs sampling as a parameter estimation
EXTRACTIVE
In this study, we focused on the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research. we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter. We succeeded in discovering the relationship between LDA topics and paper features and also obtained the researchers' interest in research field. According to our studies, some issues require further research, which can be very effective and attractive for the future. As previously mentioned, Topic modeling can find a collection of distributions over words for each topic and the relationship of topics with each document. To perform approximate inference and learning LDA, there are many inference methods for LDA topic model such as Gibbs sampling, collapsed Variational Bayes, Expectation Maximization. Gibbs sampling is a popular technique because of its simplicity and low latency. However, for large numbers of topics, Gibbs sampling can become unwieldy. In this paper, we use Gibbs Sampling in our experiment in section 5.
We extracted ISWC and WWW conferences publications from DBLP website by only considering conferences for which data was available for years 2013-2017. In total, It should be noted that in these experiments, we considered abstracts and titles from each article. In this paper, we used MALLET (http://mallet.cs.umass.edu/) to implement the inference and obtain the topic models. In addition, our full dataset is available at https://github.com/JeloH/Dataset_DBLP. The most important goal of this experiment is discover the trends of the topics and find relationship between LDA topics and paper features and generate trust tags. In this paper, all experiments were carried out on a machine running Windows 7 with CoreI3 and 4 GB memory. We learn a LDA model with 100 topics; $\alpha =0.01$, $\beta = 0.01$ and using Gibbs sampling as a parameter estimation. Related words for a topic are quite intuitive and comprehensive in the sense of supplying a semantic short of a specific research field.
1909.09551
Introduction </s> Today , ĠInternet Ġis Ġone Ġof Ġthe Ġwidest Ġavailable Ġmedia Ġworldwide . ĠIt Ġhas Ġessentially Ġbecome Ġa Ġhuge Ġhit Ġof Ġdata Ġthat Ġhas Ġthe Ġpotential Ġto Ġserve Ġmany Ġinformation Ġcent ric Ġapplications Ġin Ġour Ġlife . ĠRecommend ation Ġsystem Ġtakes Ġan Ġessential Ġpart Ġof Ġmany Ġinternet Ġservices Ġand Ġonline Ġapplications , Ġincluding Ġapplications Ġlike Ġsocial - net working Ġand Ġrecommendation Ġof Ġproducts Ġ( fil ms , Ġmusic , Ġarticles , .. i . e .). ĠRecommend ation Ġtechniques Ġhave Ġbeen Ġused Ġby Ġthe Ġmost Ġknown Ġcompanies Ġsuch Ġas ĠAmazon , ĠNetflix Ġand ĠeBay Ġto Ġrecommend Ġrele ated Ġitems Ġor Ġproducts Ġby Ġestimating Ġthe Ġprobable Ġpreferences Ġof Ġcustomers . ĠThese Ġtechniques Ġare Ġprofitable Ġto Ġboth Ġservice Ġprovider Ġand Ġuser . ĠAccording Ġto Ġper vious Ġworks , Ġtwo Ġpopular Ġapproaches Ġfor Ġbuilding Ġrecommendation Ġsystems Ġcan Ġbe Ġcategorized Ġas Ġcontent - based Ġ( CB ), Ġcollaborative Ġfiltering Ġ( CF ). </s> Content - based Ġ( CB ) Ġrecommending Ġis Ġadopted Ġfor Ġrecommendation Ġsystems Ġmodel Ġwidely , Ġwhich Ġtakes Ġadvantage Ġof Ġthe Ġproperty Ġof Ġitems Ġto Ġcreate Ġfeatures Ġand Ġcharacteristics Ġto Ġcoordinate Ġuser Ġprofiles . ĠIt Ġcan Ġbe Ġrelied Ġon Ġthe Ġproperties Ġof Ġthe Ġitems Ġthat Ġeach Ġuser Ġlikes Ġto Ġdiscover Ġwhat Ġelse Ġthe Ġuser Ġmay Ġlike . ĠOne Ġmajor Ġissue Ġof ĠCB Ġfiltering Ġmethod Ġis Ġthat Ġthe Ġrecommendation Ġsystem Ġis Ġrequired Ġto Ġgain Ġan Ġunderstanding Ġof Ġuser Ġpreferences Ġfor Ġsome Ġsorts Ġof Ġitems Ġand Ġdeploy Ġthese Ġfor Ġother Ġsorts Ġof Ġitems . </s> Nevertheless ĠCF Ġhas Ġtwo Ġwidely Ġknown Ġproblems Ġwhich Ġare Ġsp arsity Ġand Ġcold Ġstart Ġ( CS ). ĠIn Ġthe Ġrating Ġmatrix , ĠThe Ġpercentage Ġof Ġelements Ġwhich Ġget Ġvalues Ġis Ġsmall . ĠEven Ġit Ġis Ġpossible Ġthat ĠCF Ġconsiders Ġonly Ġa Ġfew Ġrating Ġfor Ġpopular Ġitems . ĠFor Ġinstance , Ġupon Ġa Ġconsiderable ĠNetflix Ġrating Ġdataset Ġwhich Ġis Ġprovided Ġfor ĠNetflix ĠPrize Ġcompetition , Ġthere Ġare Ġabout Ġ100 Ġmil ion Ġratings Ġfor Ġabout Ġ18 , 000 Ġmovies Ġthat Ġare Ġgiven Ġby Ġ480 , 000 Ġusers . ĠThe Ġpercentage Ġof Ġrating Ġmatrix Ġelements Ġwhich Ġare Ġreceived Ġratings Ġis Ġ1 . ĠWith Ġa Ġsparse Ġranking Ġmatrix Ġit Ġis Ġvery Ġchallenging Ġtopic Ġto Ġmake Ġan Ġeffective Ġrecommendation , Ġdepending Ġon Ġestimation Ġof Ġthe Ġrelationship Ġbetween Ġitems Ġand Ġusers . ĠCS Ġproblem Ġis Ġanother Ġwidely Ġknown Ġissue Ġfor ĠCF Ġapproach , Ġwhich Ġcan Ġoccur Ġon Ġnew Ġusers Ġor Ġitems . ĠIn Ġterms Ġof Ġachieving Ġan Ġeffective Ġrecommendation , ĠCF Ġapproach Ġrequires Ġeither Ġratings Ġon Ġan Ġitem Ġor Ġa Ġlarge Ġnumber Ġof Ġratings Ġfrom Ġa Ġuser . </s> Recently , Ġresearchers Ġproposed Ġvarious Ġmethods Ġbased Ġon Ġprob abil istic Ġtopic Ġmodeling Ġmethods ĠB IB REF 0 . ĠL DA Ġis Ġa Ġgener ative Ġprob abil istic Ġmodel Ġbroadly Ġused Ġin Ġthe Ġinformation Ġretrieval Ġfield . ĠResearchers Ġhave Ġused Ġtopic Ġmodeling Ġmethods Ġbased Ġon ĠL DA Ġfor Ġbuilding Ġrecommendation Ġsystems Ġin Ġvarious Ġsubjects , Ġincluding Ġapp Ġrecommendation ĠB IB REF 1 , ĠB IB REF 2 , ĠB IB REF 3 , ĠB IB REF 4 , ĠB IB REF 5 , Ġevent Ġrecommendation BI B REF 6 , ĠB IB REF 7 , ĠB IB REF 8 , ĠB IB REF 9 , ĠB IB REF 10 , ĠB IB REF 11 , ĠB IB REF 12 , ĠB IB REF 13 , ĠB IB REF 14 , ĠB IB REF 15 , Ġhashtag Ġrecommendation ĠB IB REF 16 , ĠB IB REF 17 , ĠB IB REF 18 , ĠB IB REF 19 , ĠB IB REF 20 , ĠB IB REF 21 , ĠB IB REF 22 , ĠB IB REF 23 , ĠB IB REF 24 , ĠB IB REF 25 , ĠB IB REF 26 , ĠB IB REF 27 , ĠB IB REF 28 , ĠB IB REF 29 , ĠB IB REF 30 , ĠB IB REF 31 , ĠB IB REF 32 , ĠB IB REF 33 , ĠB IB REF 34 , ĠB IB REF 35 , ĠB IB REF 36 , Ġsocial Ġnetworks Ġand Ġmedia ĠB IB REF 37 , ĠB IB REF 38 , ĠB IB REF 6 , ĠB IB REF 9 , ĠB IB REF 39 , ĠB IB REF 40 , ĠB IB REF 41 . ĠIn Ġthis Ġpaper , ĠWe Ġpresent Ġa Ġtax onomy Ġof Ġrecommendation Ġsystems Ġapplications Ġbased Ġon Ġtopic Ġmodeling Ġ( L DA ) Ġof Ġthe Ġrecent Ġresearch Ġand Ġevaluate ĠIS WC Ġand ĠWW W Ġpublications Ġin Ġcomputer Ġscience Ġbetween Ġ2013 Ġto Ġ2017 Ġfrom ĠD BL P Ġdataset . </s> Natural Ġlanguage ĠProcessing Ġand ĠL DA Ġtopic Ġmodel </s> Topic Ġmodels Ġare Ġa Ġpowerful Ġand Ġpractical Ġtool Ġfor Ġanalyzing Ġhuge Ġtext Ġdocuments Ġin ĠNatural Ġlanguage Ġprocessing . ĠTopic Ġmodels Ġcan Ġautomatically Ġcluster Ġwords Ġinto Ġtopics Ġand Ġdiscover Ġrelationship Ġbetween Ġdocuments Ġfrom Ġa Ġdataset . ĠFor Ġexample ; Ġwe Ġcan Ġassume Ġa Ġthree - topic Ġmodel Ġof Ġa ĠNews ĠDat as et , Ġincluding Ġ" s port ", Ġ" money " Ġand ĠâĢ ľ polit ic âĢ Ŀ . ĠThe Ġmost Ġcommon Ġwords Ġin Ġthe Ġsport Ġtopic Ġ( Topic Ġ1 ) Ġmight Ġbe ĠâĢ ľ gy m âĢ Ŀ , ĠâĢ ľ football âĢ Ŀ , Ġand ĠâĢ ľ ten nis âĢ Ŀ Ġand Ġin Ġaddition , Ġfor Ġpolitic Ġtopic Ġ( Topic Ġ2 ) Ġmight Ġbe ĠâĢ ľ sen ator âĢ Ŀ , ĠâĢ ľ president âĢ Ŀ , Ġand ĠâĢ ľ election âĢ Ŀ ; Ġwhile Ġthe Ġmoney Ġtopic Ġ( Topic Ġ3 ) Ġcan Ġbe Ġmade Ġup Ġof Ġwords Ġsuch Ġas ĠâĢ ľ dollar âĢ Ŀ , ĠâĢ ľ currency âĢ Ŀ , Ġand ĠâĢ ľ euro âĢ Ŀ . ĠFigure Ġ1 ; Ġshow Ġa Ġsimple Ġexample Ġfor Ġunderstanding Ġa Ġtopic Ġdiscovery Ġfrom Ġgroup Ġof Ġwords . ĠL DA Ġis Ġa Ġpopular Ġtechnique Ġto Ġsemantic Ġanalysis Ġin Ġtopic Ġmodeling Ġand Ġtext Ġmining . ĠL DA Ġcan Ġapply Ġin Ġa Ġdiversity Ġof Ġtext - information Ġto Ġevaluate Ġtopic Ġtrends Ġover Ġtime Ġand Ġanalyze Ġlarge Ġnumbers Ġof Ġdocuments . </s> In - process Ġdetail Ġfor ĠL DA , Ġdefined Ġa Ġcorpus Ġ( text ) Ġas Ġ$ D ={ d _{ 1 }, d _{ 2 }, d _{ 3 }, .... , d _{ M }} $ Ġwhere ĠM Ġis Ġnumber Ġof Ġtext Ġdocuments Ġand Ġ$ d _{ M } $ Ġis Ġa Ġnumber Ġof Ġtext Ġdocuments Ġin Ġthe Ġcorpus . ĠA Ġdocument Ġis Ġa Ġseries Ġof ĠN Ġwords Ġden oted Ġby Ġ$ W ={ w _{ 1 }, w _{ 2 }, w _{ 3 }, .... , w _{ N }} $ Ġ, Ġwhere Ġ$ w _{ N } $ Ġis Ġthe Ġ$ N th $ Ġword Ġin Ġthe Ġsequence Ġof Ġtext Ġdocument . ĠIn Ġaddition , Ġz Ġis Ġa Ġlatent Ġvariable Ġrepresenting Ġthe Ġhidden Ġtopic Ġassociated Ġwith Ġeach Ġshowed Ġword . ĠThe Ġgener ative Ġprocedure Ġfor ĠL DA , Ġformally Ġdefined Ġas : </s> For Ġtopic Ġindex Ġ$ k \ in Ġ\ left \ l brace Ġ1 , 2 ,... , k \ right \ r brace Ġ$ Ġi . ĠSelected Ġa Ġword Ġdistribution Ġ$\ beta Ġ_ { k } ~ Dir \ left (\ eta Ġ\ right ) $ </s> For Ġtext Ġdocument Ġ$ ĠD \ in Ġ\ left \ l brace Ġ1 , 2 ,... , d \ right \ r brace Ġ$ Ġi . Se lected Ġa Ġtopic Ġdistribution Ġ$\ _{ k } ~ Dir \ left (\ eta Ġ\ right ) $ Ġii . For Ġ$ n \ in Ġ\ left \ l brace Ġ1 , .... , N _{ d }\ right \ r brace Ġ$ Ġword Ġa . Se lected Ġa Ġtopic Ġassignment Ġ$ z _{ dn } ~ Mult \ left (\ the ta Ġ_ { d }\ right ) $ Ġb . Se lected Ġa Ġword Ġ$ w _{ dn } ~ Mult \ left (\ beta Ġ_ { z _{ d , n }} \ right ) $ </s> Mult () Ġis Ġa Ġmult in omial Ġdistribution , Ġand Ġ$ Dir () $ Ġis Ġa ĠDir ich let Ġdistribution Ġwhich Ġis Ġa Ġprior Ġdistribution Ġof ĠMult (), $ \ alpha Ġ$ Ġand Ġ$\ the ta Ġ$ Ġare Ġhyper param eters . </s> Natural Ġlanguage ĠProcessing Ġand ĠL DA Ġtopic Ġmodel Ġ: :: ĠGibbs Ġsampling Ġand ĠLearning ĠL DA </s> As Ġpreviously Ġmentioned , ĠTopic Ġmodeling Ġcan Ġfind Ġa Ġcollection Ġof Ġdistributions Ġover Ġwords Ġfor Ġeach Ġtopic Ġand Ġthe Ġrelationship Ġof Ġtopics Ġwith Ġeach Ġdocument . ĠTo Ġperform Ġapproximate Ġinference Ġand Ġlearning ĠL DA , Ġthere Ġare Ġmany Ġinference Ġmethods Ġfor ĠL DA Ġtopic Ġmodel Ġsuch Ġas ĠGibbs Ġsampling , Ġcollapsed ĠVari ational ĠBay es , ĠExpect ation ĠMaxim ization . ĠGibbs Ġsampling Ġis Ġa Ġpopular Ġtechnique Ġbecause Ġof Ġits Ġsimplicity Ġand Ġlow Ġlatency . ĠHowever , Ġfor Ġlarge Ġnumbers Ġof Ġtopics , ĠGibbs Ġsampling Ġcan Ġbecome Ġunw ield y . ĠIn Ġthis Ġpaper , Ġwe Ġuse ĠGibbs ĠSam pling Ġin Ġour Ġexperiment Ġin Ġsection Ġ5 . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch </s> In Ġthis Ġsection , Ġwe Ġconsidered Ġsix Ġrecommendation Ġsystems Ġbased Ġon ĠL DA Ġwhich Ġincludes : Ġscientific Ġpaper Ġrecommendation ĠB IB REF 42 , ĠB IB REF 43 , ĠB IB REF 44 , ĠB IB REF 45 , ĠB IB REF 46 , ĠB IB REF 47 , ĠB IB REF 48 , ĠB IB REF 49 , Ġmusic Ġand Ġvideo Ġrecommendation ĠB IB REF 50 , ĠB IB REF 51 , ĠB IB REF 52 , ĠB IB REF 53 , ĠB IB REF 54 , ĠB IB REF 55 , ĠB IB REF 56 , ĠB IB REF 57 , ĠB IB REF 58 , ĠB IB REF 59 , ĠB IB REF 60 , Ġlocation Ġrecommendation BI B REF 61 , ĠB IB REF 37 , ĠB IB REF 62 , ĠB IB REF 63 , ĠB IB REF 64 , ĠB IB REF 65 , ĠB IB REF 66 , Ġtravel Ġand Ġtour Ġrecommendation BI B REF 67 , ĠB IB REF 38 , ĠB IB REF 68 , ĠB IB REF 69 , Ġapp Ġrecommendation BI B REF 1 , ĠB IB REF 2 , ĠB IB REF 3 , ĠB IB REF 4 , ĠB IB REF 5 , Ġfriend Ġrecommendation ĠB IB REF 41 , ĠB IB REF 70 , ĠB IB REF 71 , Ġas Ġshown Ġin ĠFigure Ġ2 . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġscientific Ġpaper Ġrecommendation </s> In Ġrecent Ġyears Ġa Ġconsiderable Ġamount Ġof Ġresearch Ġhas Ġaddressed Ġthe Ġtask Ġof Ġdefining Ġmodels Ġand Ġsystems Ġfor Ġscientific Ġpapers Ġrecommendation ; Ġthis Ġtrend Ġhas Ġemerged Ġas Ġa Ġnatural Ġconsequence Ġof Ġthe Ġincreasing Ġgrowth Ġof Ġthe Ġnumber Ġof Ġscientific Ġpublications . ĠFor Ġexample , ĠYou n Ġand Ġet Ġal , Ġproposed Ġan Ġapproach Ġto Ġscientific Ġarticles 're comm end ation Ġof Ġuser 's Ġinterests Ġbased Ġon Ġa Ġtopic Ġmodeling Ġframework . ĠThe Ġauthors , Ġused Ġa ĠL DA Ġmodel Ġin Ġorder Ġto Ġextract Ġthe Ġtopics Ġof Ġthe Ġfollow ees 't we ets Ġ( follow ed ĠTw itt erers ) Ġand Ġthe Ġpaper Ġtitles BI B REF 49 . ĠThey Ġapply Ġthe ĠTwitter - L DA Ġalgorithm Ġsimultaneously Ġon Ġthe Ġfollow ees ' Ġtweets Ġand Ġthe Ġpaper Ġtitles Ġwith Ġthe Ġnumber Ġof Ġtopics Ġset Ġto Ġ200 , Ġthey Ġutilized Ġthe Ġintersection Ġof Ġtopics Ġfound Ġin Ġboth Ġpaper Ġtitles Ġand Ġfollow ees 's Ġtweets . ĠEach Ġfollow ee Ġof Ġa Ġuser Ġis Ġranked Ġas Ġfollows : </s> where Ġ$ T _ f $ den otes Ġall Ġtweets Ġby Ġa Ġfollow ee , </s> ${ Topics } _ p , \ Ġ$ den otes Ġthe Ġset Ġof Ġtopics Ġdefining Ġthe Ġtitles Ġof Ġscientific Ġarticles , </s> ${ Topics } _ f , \ Ġ$ den otes Ġthe Ġset Ġof Ġtopics Ġdefining Ġthe Ġtweets Ġof Ġa Ġfollow ee Ġand Ġ$ n \ left ( t , T _ f \ right )\ Ġ$ the Ġnumber Ġof Ġtimes Ġa Ġparticular Ġtopic Ġ` t ' Ġfrom Ġwithin Ġoccurs Ġamong Ġthe Ġtweets Ġof Ġa Ġfollow ee . ĠBased Ġon Ġthe Ġranking Ġscores Ġof Ġall Ġfollow ees Ġof Ġa Ġparticular Ġuser , and Ġobtained Ġtop - k Ġresearchers Ġfollowed Ġby Ġa Ġtarget Ġuser . ĠFor Ġevaluation Ġapproach , Ġconsidered ĠD BL P Ġdatabase Ġas Ġa Ġlarge Ġacademic Ġb ibli ographic Ġnetwork . </s> Also , Ġsome Ġresearchers , Ġintroduced Ġa Ġcombined Ġmodel Ġbased Ġon Ġtraditional Ġcollaborative Ġfiltering Ġand Ġtopic Ġmodeling Ġand Ġdesigned Ġa Ġnovel Ġalgorithm Ġto Ġscientific Ġarticles Ġrecommendation Ġfor Ġusers Ġfrom Ġan Ġonline Ġcommunity , Ġcalled ĠCTR Ġmodel . ĠThey Ġconsidered ĠL DA Ġto Ġinitialize Ġthe ĠCTR Ġmodel , ĠInf act Ġthey Ġcombined Ġthe Ġmatrix Ġfactor ization Ġand Ġthe ĠL DA Ġmodel , Ġand Ġis Ġshown Ġtheir Ġapproach Ġbetter Ġthan Ġthe Ġrecommendations Ġbased Ġon Ġmatrix Ġfactor ization . ĠFor Ġevaluation Ġand Ġtest , ĠUsed Ġa Ġlarge Ġdataset Ġfrom Ġa Ġb ibliography Ġsharing Ġservice Ġ( C ite UL ike ) ĠB IB REF 47 . ĠTable Ġ1 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġpaper Ġrecommendation . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġmusic Ġand Ġvideo Ġrecommendation </s> Video Ġand Ġmusic Ġrecommendation Ġhas Ġbecome Ġan Ġessential Ġway Ġfor Ġhelping Ġpeople Ġexplore Ġthe Ġvideo Ġworld Ġand Ġdiscover Ġthe Ġones Ġthat Ġmay Ġbe Ġof Ġinterest Ġto Ġthem . ĠRecently , Ġanalyze Ġuser Ġinterests Ġand Ġa Ġgood Ġvideo Ġor Ġmusic Ġrecommendation Ġin Ġinternet Ġsociety Ġis Ġa Ġbig Ġchallenge . ĠHar iri Ġand Ġet Ġal . Ġproposed Ġa Ġcombined Ġapproach Ġbased Ġon Ġcontent Ġand Ġcollaborative Ġfiltering Ġmethods Ġfrom Ġthe Ġsequence Ġof Ġsongs Ġlistened Ġto Ġgenerate Ġmusic Ġrecommendation . ĠThey Ġapplied Ġa ĠL DA Ġmodel Ġto Ġreduce Ġthe Ġdimension ality Ġof Ġthe Ġfeature Ġand Ġobtain Ġthe Ġhidden Ġrelationships Ġbetween Ġsongs Ġand Ġtags . ĠThey Ġcollected Ġ218 , 261 Ġdistinct Ġsongs Ġfrom Ġ" Art Ġof Ġthe ĠMix " Ġwebsite Ġfor Ġevaluation Ġtheir Ġapproach ĠB IB REF 60 . ĠYan Ġand Ġet Ġal , Ġfocused Ġon Ġthe Ġefficiency Ġof Ġusers ' Ġinformation Ġcontent Ġon Ġthe Ġonline Ġsocial Ġnetwork Ġand Ġprovided Ġa Ġsolution Ġas Ġa Ġpersonalized Ġvideo Ġrecommendation Ġwith Ġconsidering Ġusers ' Ġcross - network Ġsocial Ġand Ġcontent Ġdata . ĠThey Ġapplied Ġa Ġtopic Ġmodel Ġbased Ġon ĠL DA Ġfor Ġeach Ġuser , Ġthat Ġuser Ġas Ġdocument Ġand Ġuser 's Ġhasht ags Ġas Ġword , Ġwith Ġconsidering Ġuser 's Ġinformation Ġfrom ĠTwitter ĠB IB REF 52 . ĠThey Ġderived ĠTwitter Ġuser Ġtopic Ġdistributions Ġand Ġobserved Ġuser - video Ġinteractions Ġon ĠYouTube , Ġand Ġpresented Ġa Ġsolution Ġfor Ġuser Ġpreference Ġtransfer : </s> $ u ^{ T _ t } _ i $, ĠTwitter Ġuser Ġtweet Ġtopic Ġdistribution Ġmatrix Ġ$ u ^{ T _ t } $ Ġ= Ġ$\ l brace Ġ$$ u ^{ T _ t } _ i $ Ġ; ... ; $ \ Ġu ^{ T _ t } _{ | U | } $ Ġ$\ r brace Ġ$ , Ġ$ u ^{ T _ f } _ i , $ ĠTwitter Ġuser Ġsocial Ġtopic Ġdistribution Ġmatrix Ġ$ u ^{ T _ f } $ = Ġ$\ l brace Ġ$$ u ^{ T _ f } _ i $ Ġ; ... ; $ \ Ġu ^{ T _ f } _{ | U | } $ Ġ$\ r brace Ġ$ , Ġ$ W _ 1 $ and Ġ$ W _ 2 , \ Ġ$ with Ġobservations Ġof Ġthe Ġoverl apped Ġuser 's ĠTwitter Ġand ĠYouTube Ġbehaviors , Ġ$\ math rm Ġ{\ O mega Ġ} $ Ġas Ġthe Ġcollection Ġof Ġall Ġthe Ġobserved Ġuser - video Ġpairs , Ġ$\ eta Ġ$ Ġis Ġa Ġtrade - off Ġparameter Ġto Ġbalance Ġthe Ġcontribution Ġof Ġdifferent Ġtypes Ġof Ġuser 's Ġbehaviors Ġon ĠTwitter , Ġwhere Ġ$ v _ j $ Ġis Ġthe Ġ$ j ^{ th } $ row Ġof Ġ$ V $ Ġ, Ġ$ L _ j $ Ġis Ġthe Ġ$ j ^{ th } $ column Ġof ĠL , Ġ$ L _{ jj } $ Ġis Ġthe Ġentry Ġlocated Ġin Ġthe Ġ$ j ^{ th } $ column Ġand Ġ$ j ^{ th } $ Ġrow Ġof ĠL . ĠBased Ġon , Ġupdated Ġ$ v _ j $ Ġand Ġ$ W _ 1 $, Ġ$ W _ 2 $ Ġiter atively Ġuntil Ġconvergence Ġor Ġmaximum Ġiteration . ĠThe Ġupdate Ġrules Ġare : </s> where Ġ$\ gam ma Ġ$ Ġdenotes Ġthe Ġlearning Ġrate . </s> With Ġthe Ġderived Ġtransfer Ġmat rices Ġ$ W _ 1 $, Ġ$ W _ 2 $ Ġand Ġvideo Ġlatent Ġfactor Ġrepresentations ĠV , Ġgiven Ġa Ġtest Ġuser Ġ$ u _ i $ Ġwith Ġhis / her Ġtweeting Ġactivity , Ġfriend Ġcollection , Ġand Ġthe Ġcorresponding ĠTwitter Ġtopical Ġdistributions Ġ$ u ^{ T _ t } _ i $ Ġ, Ġ$ u ^{ f _ i } _ i $ Ġ, Ġwe Ġcan Ġestimate Ġ$ u _ i $ Ġpreferences Ġon ĠYouTube Ġvideos Ġas : </s> For Ġtest Ġand Ġexperiment , Ġutilized Ġtheir Ġapproach Ġon ĠYouTube - Twitter Ġdataset Ġthat Ġinclude Ġ9 , 253 , 729 Ġtweeting Ġbehaviors Ġand Ġ1 , 09 7 , 98 2 Ġvideo - related Ġbehavior Ġand Ġshowed Ġcombining Ġauxiliary Ġnetwork Ġinformation Ġand Ġutilizing Ġa Ġcross - network ĠCollabor ative Ġcan Ġlead Ġto Ġgenerating Ġnovel Ġrecommendations Ġand Ġincreasing Ġsatisfaction Ġfor Ġusers . </s> In Ġaddition , Ġsome Ġresearchers ; Ġproposed Ġan Ġapproach Ġbased Ġon ĠCollabor ative ĠFilter Ġ( CF ) ĠAl gorithm Ġand Ġutilized Ġthe Ġapplication Ġof Ġsession Ġvariety Ġand Ġtemporal Ġcontext . ĠThey Ġapplied Ġa ĠL DA Ġmodel Ġto Ġtemporal Ġproperties Ġextraction Ġof Ġsessions Ġand Ġthat Ġconsidering Ġsessions Ġas Ġdocuments Ġand Ġsongs Ġas Ġwords . ĠFor Ġevaluation Ġof Ġthis Ġapproach , Ġthey Ġused ĠLast . fm Ġdataset ( log ) Ġthat Ġincludes Ġ19 , 150 , 8 68 Ġentries Ġfrom Ġ9 92 Ġusers . ĠResults Ġshowed Ġthat Ġthe Ġapproach Ġwith Ġusing Ġtemporal Ġinformation Ġcan Ġincrease Ġthe Ġaccuracy Ġof Ġmusic Ġrecommendations ĠB IB REF 54 . </s> In Ġaddition , Ġother Ġresearchers Ġused Ġa Ġdynamic Ġframework Ġbased Ġon Ġfour Ġaspects Ġof Ġuser 's Ġpreference Ġ( coll abor ative Ġaspect , Ġcontent Ġaspect , Ġpopularity Ġaspect , Ġand Ġrandom ization Ġaspect ) Ġfor Ġmovie Ġrecommendation . ĠThe Ġauthors Ġapplied Ġa Ġlinear Ġcombination Ġmodel Ġto Ġgenerate Ġthe Ġfinal Ġrecommendation Ġlist ĠB IB REF 51 . ĠHu Ġet Ġal , Ġproposed Ġa Ġnovel Ġtopic Ġmodeling Ġto Ġaudio Ġretrieval , Ġcalled ĠGa ussian L DA . ĠIn Ġgeneral , Ġin Ġthis Ġapproach Ġit Ġwas Ġassumed Ġthat Ġeach Ġaudio Ġdocument Ġincludes Ġvarious Ġlatent Ġtopics Ġand Ġeach Ġtopic Ġconsidered Ġas Ġa ĠGa ussian Ġdistribution . ĠThey Ġprepared Ġ12 14 Ġaudio Ġdocuments Ġ( length : Ġbetween Ġ0 . 82 Ġs Ġto Ġ1 Ġmin ), Ġthat Ġeach Ġaudio Ġdocument Ġis Ġrelated Ġwith Ġa Ġcategory Ġin Ġdifferent Ġsubject Ġthat Ġincludes : Ġbell , Ġriver , Ġrain , Ġlaugh , Ġdog , Ġgun Ġand Ġso Ġon . ĠTheir Ġresults Ġshowed Ġthat Ġthe ĠGa ussian L DA Ġmodel Ġsignificantly Ġoutper form Ġthe Ġstandard ĠL DA Ġtopic Ġmodel BI B REF 56 . ĠTable Ġ2 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġmusic Ġand Ġvideo Ġrecommendation . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġlocation Ġrecommendation </s> Recommend ation Ġsystems Ġbased Ġon Ġlocation Ġcan Ġsuggest Ġa Ġset Ġof Ġplaces Ġthat Ġusers Ġmay Ġbe Ġinterested Ġin , Ġbased Ġon Ġtheir Ġhistory Ġand Ġbehavior Ġanalysis . ĠL DA Ġcan Ġalso Ġbe Ġused Ġfor Ġlocation Ġrecommendation . ĠKur ash ima Ġet Ġal , Ġproposed Ġa Ġnovel Ġtopic Ġmodel Ġfor Ġrecommending Ġnew Ġlocations Ġto Ġvisit , Ġcalled ĠGeo ĠTopic ĠModel . ĠThis Ġmodel Ġcan Ġpredict Ġuser 's Ġinterest Ġand Ġthe Ġuser 's Ġspatial Ġarea Ġbased Ġon Ġfeatures Ġof Ġvisited Ġlocations . ĠThey Ġused ĠT abel og - based Ġ( table log . com ) Ġand ĠFlickr - based Ġreal - location Ġlog Ġdata Ġfor Ġevaluation Ġof Ġtheir Ġapproach . ĠThey Ġfound Ġthat Ġthis Ġmodel Ġcan Ġdiscover Ġlatent Ġtopics Ġrelated Ġto Ġart , Ġgreat Ġviews , Ġnature , Ġatmosphere , Ġand Ġconstruction Ġand Ġother Ġfrom Ġlogs Ġof Ġvisited Ġplaces ĠB IB REF 63 . ĠTable Ġ3 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġlocation Ġrecommendation . </s> L iu Ġand Ġet Ġal , Ġinvestigated Ġthe ĠPO I Ġrecommendation Ġissue Ġin ĠL BS Ns Ġby Ġmining Ġtextual Ġinformation Ġand Ġproposed Ġa Ġ' Topic Ġand ĠLocation - aware ' Ġprob abil istic Ġmatrix Ġfactor ization Ġ( TL - PM F ) Ġmethod Ġfor ĠPoint - of - Interest Ġrecommendation Ġto Ġdiscover Ġpersonalized Ġrecommendations Ġfrom Ġfavorite Ġplaces ĠB IB REF 37 . ĠThe Ġdistribution Ġover Ġthe Ġobserved Ġratings Ġas Ġwell Ġas Ġthe Ġtextual Ġinformation Ġis : </s> Where , Ġ${ r } _{ ij } $ Ġbe Ġthe Ġrating Ġof Ġuser Ġ${ u } _{ i } $ Ġfor Ġ${ PO I } _{ { c } _{ j }} { C } _{ j } $, </s> ${ U } _{ i } $ and Ġ${ C } _{ i } $ Ġare Ġthe Ġuser Ġand ĠPO I Ġlatent Ġfeature Ġspace Ġvector Ġrespectively , </s> $ \ math cal Ġ{ N }\ left (\ math rm Ġ{\ text rm Ġ{ - }} \ right | , {\ s igma Ġ} ^{ 2 }) $ Ġis Ġa ĠGa ussian Ġdistribution Ġwith Ġmean Ġand Ġvariance Ġ${ \ s igma Ġ} ^{ 2 } $, </s> ${ I } _{ ij } $ Ġis Ġthe Ġindicator Ġfunction , </s> Function Ġ$ f ( U _ i , C _{ j , { TL } _{ ij }} ) $ Ġis Ġto Ġapproximate Ġthe Ġrating Ġof Ġuser Ġ$ u _ i $ Ġfor Ġ${ PO I } _{ c j } $. </s> they Ġanalyzed Ġthe Ġtopic Ġcharacteristics Ġfrom ĠPO Is Ġacross Ġvarious Ġgeographical Ġareas . ĠThe Ġexperiments Ġwere Ġconducted Ġon Ġa Ġlarge Ġreal - world ĠL BS N Ġdataset ; Ġthey Ġanalyzed Ġthe Ġtopic Ġcharacteristics Ġfrom ĠPO Is Ġacross Ġvarious Ġgeographical Ġareas . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġfriend Ġrecommendation </s> Friend Ġrecommendation Ġis Ġa Ġpopular Ġmethod Ġto Ġhelp Ġusers Ġto Ġmake Ġnew Ġfriends Ġand Ġdiscover Ġinteresting Ġinformation . ĠFriend Ġrecommendation Ġis Ġa Ġrelative Ġchallenging Ġissue Ġcontrasted Ġwith Ġgroup Ġor Ġitem Ġrecommendations Ġin Ġonline Ġsocial Ġnetworks BI B REF 70 , ĠB IB REF 41 , ĠB IB REF 71 . ĠTo Ġaddress Ġthis Ġchallenge Ġissue , Ġa Ġrecent Ġwork Ġin Ġ[ 79 ] Ġproposed Ġa Ġfriend Ġrecommendation Ġbased Ġon ĠL DA , Ġwhich Ġcontains Ġtwo Ġstages : Ġfirst Ġstep , Ġthey Ġapplied Ġtag - user Ġinformation Ġto Ġproduce Ġa Ġpossible Ġfriend Ġlist Ġand Ġthen Ġthey Ġcreated Ġa Ġtopic Ġmodel Ġto Ġdemonstrate Ġthe Ġrelationship Ġbetween Ġuser 's Ġfriend Ġmaking Ġbehaviour Ġand Ġimage Ġfeatures . ĠThey Ġapplied Ġexperiments Ġon Ġthe ĠFlickr Ġas Ġa Ġstandard Ġdataset Ġand Ġshowed Ġthat Ġtheir Ġrecommends Ġfriends Ġmore Ġquickly Ġthan Ġtraditional Ġmethods . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel ĠBased Ġon Ġtravel Ġand Ġtour Ġrecommendation </s> Def initely , Ġrecommendation Ġsystems Ġcan Ġhave Ġa Ġsignificant Ġimpact Ġto Ġbuild Ġa Ġsmart Ġtravel Ġrecommendation . ĠMany Ġdifferent Ġtechniques Ġhave Ġrecently Ġbeen Ġdeveloped Ġto Ġsupport Ġtravel Ġrecommendation Ġbased Ġon Ġdifferent Ġkinds Ġof Ġdata . ĠFor Ġexample , Ġin ĠB IB REF 67 , Ġthe Ġauthors Ġproposed Ġa Ġnovel Ġgener ative Ġprob abil istic Ġmodel Ġnamed Ġsoc o L DA Ġwith Ġheter ogeneous Ġsocial Ġinfluence Ġto Ġbetter Ġcapture Ġusers 's Ġtravel Ġinterests . ĠThey Ġintroduced Ġthe Ġframework Ġof Ġtravel - package Ġrecommendation Ġnamed Ġsoc o Travel er , Ġwhich Ġapply s Ġsoc o L DA Ġto Ġshow Ġa Ġuser 's Ġtravel Ġinterests Ġin Ġtopic Ġspace Ġand Ġfind Ġsimilar Ġusers Ġto Ġproduce Ġrecommendations Ġwith Ġuser - based Ġcollaborative Ġfiltering . ĠIn Ġaddition , Ġin ĠB IB REF 68 , Ġthe Ġauthors Ġproposed Ġa Ġframework Ġto Ġsuggest Ġtop - k Ġtours Ġwith Ġhighest Ġmarks Ġfor Ġa Ġuser Ġby Ġusing Ġthe Ġphotos Ġshared Ġby Ġother Ġusers Ġin Ġan Ġonline Ġsocial Ġnetwork . ĠTable Ġ4 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġtravel Ġand Ġtour Ġrecommendation . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġapp Ġrecommendation </s> Currently , Ġa Ġwide Ġrange Ġof Ġrecommendation Ġapproaches Ġhave Ġbeen Ġproposed Ġand Ġapplied Ġto Ġrecommend Ġmobile Ġapps . ĠFor Ġexample , ĠIn ĠB IB REF 3 , Ġproposed Ġa Ġnovel Ġprob abil istic Ġmodel , Ġnamed ĠGoal - oriented ĠExpl or atory ĠModel Ġ( G EM ), Ġto Ġcombine Ġthe Ġidentification Ġof Ġexplor atory Ġbehavior Ġand Ġmobile Ġapp Ġrecommendations Ġinto Ġa Ġunified Ġframework . ĠThe Ġauthors , Ġemployed Ġthe Ġidea Ġof ĠL DA Ġto Ġdesign Ġa Ġtopic Ġmodel Ġto Ġcluster Ġitems Ġinto Ġgoals Ġand Ġidentify Ġthe Ġpersonal Ġdistribution Ġof Ġgoals Ġfor Ġeach Ġuser Ġand Ġdeveloped Ġan Ġeffective Ġand Ġefficient Ġalgorithm , Ġwhich Ġintegrates ĠExpect ation - Max im ization Ġ( EM ) Ġalgorithm Ġwith Ġcollapsed ĠGibbs Ġsampling Ġfor Ġmodel Ġlearning . ĠThey Ġcollected Ġa Ġmobile Ġapp Ġdataset Ġfrom ĠQ ih oo Ġ360 ĠMobile ĠAssistant , Ġan Ġopen Ġmobile Ġapp Ġplatform Ġin ĠChina Ġfor ĠAndroid Ġusers . ĠLin Ġet Ġal , Ġinvestigated Ġthe Ġcold - start Ġissue Ġwith Ġusing Ġthe Ġsocial Ġinformation Ġfor ĠApp Ġrecommendation Ġin ĠTwitter Ġand Ġused Ġa ĠL DA Ġmodel Ġto Ġdiscovering Ġlatent Ġgroup Ġfrom Ġ" Twitter Ġpersonalities " Ġto Ġrecommendations Ġdiscovery . ĠThis Ġapproach Ġis Ġbased Ġon Ġa Ġsimple Ġ" aver aging " Ġmethod Ġwhere Ġthe Ġprobability Ġof Ġhow Ġlikely Ġthe Ġtarget Ġuser Ġwill Ġlike Ġthe Ġapp Ġis Ġthe Ġexpectation Ġof Ġhow Ġthe ĠTwitter - follow ers Ġlike Ġthe Ġapp . ĠGiven Ġa Ġset Ġof ĠTwitter - follow ers Ġ$ T $, Ġthe Ġprobability Ġthat Ġuser Ġ$ u $ Ġlikes Ġapp Ġa Ġis Ġdefined Ġas Ġfollows : </s> where ĠT ( a ) Ġis Ġthe Ġset Ġof Ġpossible ĠTwitter - follow ers Ġfollowing Ġapp Ġa , Ġin Ġwhich Ġassume Ġthat : </s> ( i ) ĠTwitter - follow ers Ġare Ġexamined Ġonce Ġat Ġa Ġtime Ġto Ġmake Ġa Ġdecision Ġabout Ġwhether Ġan Ġapp Ġis Ġliked Ġor Ġdisliked . </s> ( ii ) Ġwhen Ġthe ĠTwitter Ġfollower Ġis Ġknown , Ġthe Ġjudgement Ġdoes Ġnot Ġdepend Ġon Ġthe Ġapp Ġany Ġmore , Ġi . e ., </s> ( iii ) Ġthe Ġfact Ġthat Ġgiven Ġa Ġuser Ġand Ġan Ġapp , Ġthere Ġis Ġno Ġjudgement Ġinvolved , Ġi . e ., </s> ( iv ) Ġthe Ġfact Ġthat Ġan Ġapp Ġhas Ġa Ġgiven ĠTwitter Ġfollower Ġis Ġindependent Ġfrom Ġthe Ġuser , Ġi . e ., </s> Equ ation Ġ( DIS PLAY _ FORM 24 ) Ġis Ġthen Ġreduced Ġto Ġthe Ġestimation Ġof Ġtwo Ġquantities : </s> 1 . ĠThe Ġprobability Ġthat Ġuser Ġu Ġlikes Ġapp Ġa Ġgiven Ġthat Ġapp Ġa Ġhas ĠTwitter - f ollower Ġt , Ġi . e ., Ġ$ p ( + | t , \ Ġu ) $, Ġand </s> 2 . ĠThe Ġprobability Ġof Ġconsidering ĠTwitter - f ollower Ġt Ġgiven Ġapp Ġa , Ġi . e ., Ġ$ p ( t | a ) $ </s> $ p ( + | t , \ Ġu ) $ Ġis Ġstraightforward Ġto Ġestimate Ġas Ġit Ġcan Ġbe Ġrewritten Ġas : </s> where Ġ$ p ( +, \ Ġt | u ) $ Ġand Ġ$ p ( -, \ Ġt | u ) $ Ġare Ġderived Ġfrom ĠL DA , Ġwhich Ġis Ġthe Ġprobability Ġthat ĠTwitter - f ollower Ġt Ġoccurs Ġin Ġan Ġapp Ġthat Ġis Ġliked Ġ( or Ġdisliked ) Ġby Ġuser Ġu . </s> For Ġtest Ġand Ġexperiment , Ġthey Ġconsidered ĠApple 's ĠiTunes ĠApp ĠStore Ġand ĠTwitter Ġas Ġdataset . ĠExperimental Ġresults Ġshow , Ġtheir Ġapproach Ġsignificantly Ġbetter Ġthan Ġother Ġstate - of - the - art Ġrecommendation Ġtechniques ĠB IB REF 74 . </s> In ĠB IB REF 4 , Ġthe Ġauthors Ġproposed Ġan Ġallocation - based Ġprob abil istic Ġmechanism Ġthat Ġconsiders Ġmultiple Ġuser - app Ġfactors Ġto Ġhelp Ġusers Ġwith Ġapp Ġrecommendations . ĠThis Ġframework Ġthat Ġcan Ġcapture Ġgeographical Ġinfluences Ġon Ġusage Ġbehaviors Ġand Ġeffectively Ġmodel Ġuser Ġmobility Ġpatterns , Ġwhich Ġin Ġturn Ġaffect Ġapp Ġusage Ġpatterns . ĠThey Ġused ĠGibbs Ġsampling Ġto Ġapproximately Ġestimate Ġand Ġinfer Ġthe Ġparameters Ġof ĠL DA . ĠThe Ġauthors Ġmeasure Ġthe Ġsimilarity Ġof Ġtwo Ġlocation Ġblocks Ġby Ġthe Ġsimilarity Ġof Ġapp Ġusage Ġpattern , Ġwhich Ġis Ġcalculated Ġby ĠPearson 's Ġcorrelation Ġsimilarity ĠB IB REF 75 : </s> Specifically , </s> defined Ġ$ c ({ \ mu Ġ} _ i , {\ alpha Ġ} _ j , l _ x ) $ = 1 , Ġif Ġuser Ġ${ \ mu Ġ} _ i $ has Ġlaunched Ġapp Ġ${ \ alpha Ġ} _ j $ at Ġlocation Ġblock Ġ$ l _ x $. </s> Otherwise , Ġ$ c ({ \ mu Ġ} _ i , {\ alpha Ġ} _ j , l _ x ) $ = 0 . ĠTherefore , Ġthe Ġnumber Ġof Ġusers Ġwho Ġhave Ġlaunched Ġ${ \ alpha Ġ} _ j $ Ġat Ġlocation Ġblock Ġ$ l _ x $ Ġis Ġ$ I _{ aj , l _ x } = \ sum Ġ_ {{ \ mu Ġ} _ i \ in ĠU }{ c ({ \ mu Ġ} _ i , {\ alpha Ġ} _ j , l _ x )} $. </s> where Ġ$\ over line { I _{ l _ x }} $ Ġdenotes Ġaverage Ġmobile Ġapp Ġinfluences Ġat Ġlocation Ġblock Ġ$ l _ x $. </s> To Ġdetermine Ġwhether Ġa Ġlocation Ġblock Ġ$ l _ z $ Ġbelongs Ġto Ġa Ġlocation Ġ$ L $, </s> They Ġdefine Ġthe Ġapp Ġusage Ġpattern Ġcoefficient Ġas Ġ$ r _{ l _ x } $ for Ġgeographical Ġblock Ġ$ l _ x $. ĠThe Ġinitial Ġcollaborative Ġfilter Ġcoefficient Ġis Ġthe Ġaverage Ġmobile Ġapp Ġinfluence . ĠTo Ġdecide Ġwhether Ġlocation Ġblock Ġ$ l _ z $ Ġbelongs Ġto Ġgeographical Ġregion Ġ$ L $. ĠAlso , Ġthey Ġcalculate Ġits Ġcollaborative Ġfilter Ġcoefficient Ġas Ġfollows Ġ: </s> where Ġ${ s } _{ L } $ Ġis Ġthe Ġnumber Ġof Ġlocation Ġblocks Ġin Ġgeographical Ġregion Ġ$ L $. ĠIf Ġthe Ġvalue Ġof Ġ$ r _{ l _ z } $$ > $$ \ Ġr _{ th } $, Ġwhere Ġ$ r _{ th } $ Ġis Ġa Ġpred efined Ġthreshold Ġvalue , Ġlocation Ġblock Ġ$ l _ z $ will Ġbe Ġincluded Ġin Ġ$ L $. </s> Exper iment : ĠSem antic ĠDiscovery Ġand Ġresearchers Ġbehavior Ġanalysis Ġ: :: ĠDat as et </s> We Ġextracted ĠIS WC Ġand ĠWW W Ġconferences Ġpublications Ġfrom ĠD BL P Ġwebsite Ġby Ġonly Ġconsidering Ġconferences Ġfor Ġwhich Ġdata Ġwas Ġavailable Ġfor Ġyears Ġ2013 - 2017 . ĠIn Ġtotal , ĠIt Ġshould Ġbe Ġnoted Ġthat Ġin Ġthese Ġexperiments , Ġwe Ġconsidered Ġabstract s Ġand Ġtitles Ġfrom Ġeach Ġarticle . ĠIn Ġthis Ġpaper , Ġwe Ġused ĠM AL LET Ġ( http :// mal let . cs . um ass . edu /) Ġto Ġimplement Ġthe Ġinference Ġand Ġobtain Ġthe Ġtopic Ġmodels . ĠIn Ġaddition , Ġour Ġfull Ġdataset Ġis Ġavailable Ġat Ġhttps :// github . com / J elo H / Dat as et _ D BL P . ĠThe Ġmost Ġimportant Ġgoal Ġof Ġthis Ġexperiment Ġis Ġdiscover Ġthe Ġtrends Ġof Ġthe Ġtopics Ġand Ġfind Ġrelationship Ġbetween ĠL DA Ġtopics Ġand Ġpaper Ġfeatures Ġand Ġgenerate Ġtrust Ġtags . </s> Exper iment : ĠSem antic ĠDiscovery Ġand Ġresearchers Ġbehavior Ġanalysis Ġ: :: ĠParam eter ĠSettings </s> In Ġthis Ġpaper , Ġall Ġexperiments Ġwere Ġcarried Ġout Ġon Ġa Ġmachine Ġrunning ĠWindows Ġ7 Ġwith ĠCore I 3 Ġand Ġ4 ĠGB Ġmemory . ĠWe Ġlearn Ġa ĠL DA Ġmodel Ġwith Ġ100 Ġtopics ; Ġ$\ alpha Ġ= 0 . 01 $, Ġ$\ beta Ġ= Ġ0 . 01 $ Ġand Ġusing ĠGibbs Ġsampling Ġas Ġa Ġparameter Ġestimation . ĠRelated Ġwords Ġfor Ġa Ġtopic Ġare Ġquite Ġintuitive Ġand Ġcomprehensive Ġin Ġthe Ġsense Ġof Ġsupplying Ġa Ġsemantic Ġshort Ġof Ġa Ġspecific Ġresearch Ġfield . </s> Exper iment : ĠSem antic ĠDiscovery Ġand Ġresearchers Ġbehavior Ġanalysis Ġ: :: ĠSem antic Ġanalysis Ġand Ġgenerate Ġtags </s> In Ġthis Ġsection , Ġwe Ġprovide Ġthe Ġresults Ġand Ġdiscovered Ġtopics Ġof Ġ100 - top ics Ġfor ĠIS WC Ġand ĠWW W Ġconferences . </s> According Ġto ĠTable Ġ6 Ġ, Ġthe Ġfollowing Ġobservations Ġcan Ġbe Ġmade : </s> In ĠIS WC Ġconference , ĠTopic Ġ25 Ġsounds Ġconsiderably Ġmore Ġgeneric Ġand Ġis Ġconsistent Ġwith Ġ' Con cept ĠRepresent ation ' Ġin Ġgeneral , Ġand Ġmarked Ġby Ġrepresentation , Ġstructure , Ġinvestigate , Ġdetection , Ġvalues , Ġand Ġconcept . ĠAlso , Ġwe Ġcan Ġsee Ġthat Ġfrom Ġ20 Ġgenerated Ġwords Ġin ĠTopic Ġ20 , Ġsome Ġwords Ġare Ġvery Ġrelated Ġto Ġeach Ġother Ġin Ġmeans Ġsuch Ġas Ġdocuments , Ġscientific , Ġnetworks , Ġweb , Ġhealth , Ġkg Ġand Ġwe Ġfound Ġthat Ġthis Ġtopic Ġcovers Ġpapers Ġthat Ġpropose Ġmodels Ġin Ġ' Documents ĠAnalysis Ġin Ġhealth Ġresearch '. </s> In ĠWW W Ġconference , ĠTopic Ġ7 , Ġthis Ġis Ġour Ġquestion ; Ġthe Ġword Ġof Ġ' ad ' ! ĠIs Ġfor Ġ' Advertisement ' Ġor Ġ' Ad ĠH oc ĠNetwork '? ĠAs Ġwe Ġcan Ġsee , Ġthe Ġword Ġ' ad ' Ġcan Ġbe Ġrelated Ġto Ġ' Advertisement ' Ġor Ġalso Ġto Ġ' ad Ġhoc Ġnetwork '. ĠTo Ġanswer Ġthis Ġquestion , Ġit Ġis Ġvery Ġeasy Ġto Ġsee Ġthat Ġtopic Ġ7 Ġreveals Ġsocial , Ġmodeling , Ġranking , Ġquestion , Ġad , Ġbrowser . ĠIf Ġonly Ġwe Ġconsider Ġthe Ġwords Ġ' browser ', Ġnews , Ġsocial , Ġwe Ġcan Ġpredict Ġthat Ġthis Ġtopic Ġcan Ġbe Ġrelated Ġto Ġ' Advertisement ' Ġand Ġthis Ġtopic Ġcovers Ġpapers Ġthat Ġpropose Ġmethods Ġin Ġ' Question ĠAn sw ering Ġand ĠSocial ĠMedia '. </s> Discussion , ĠOpen ĠIssues Ġand ĠFuture ĠDirections </s> In Ġthis Ġstudy , Ġwe Ġfocused Ġon Ġthe ĠL DA Ġapproaches Ġto Ġrecommendation Ġsystems Ġand Ġgiven Ġthe Ġimportance Ġof Ġresearch , Ġwe Ġhave Ġstudied Ġrecent Ġimpressive Ġarticles Ġon Ġthis Ġsubject Ġand Ġpresented Ġa Ġtax onomy Ġof Ġrecommendation Ġsystems Ġbased Ġon ĠL DA Ġof Ġthe Ġrecent Ġresearch . Ġwe Ġevaluated ĠIS WC Ġand ĠWW W Ġconferences Ġarticles Ġfrom ĠD BL P Ġwebsite Ġand Ġused Ġthe ĠGibbs Ġsampling Ġalgorithm Ġas Ġan Ġevaluation Ġparameter . ĠWe Ġsucceeded Ġin Ġdiscovering Ġthe Ġrelationship Ġbetween ĠL DA Ġtopics Ġand Ġpaper Ġfeatures Ġand Ġalso Ġobtained Ġthe Ġresearchers ' Ġinterest Ġin Ġresearch Ġfield . ĠAccording Ġto Ġour Ġstudies , Ġsome Ġissues Ġrequire Ġfurther Ġresearch , Ġwhich Ġcan Ġbe Ġvery Ġeffective Ġand Ġattractive Ġfor Ġthe Ġfuture . </s> Discussion , ĠOpen ĠIssues Ġand ĠFuture ĠDirections Ġ: :: ĠTopic Ġmodeling Ġmethods Ġand Ġtraditional Ġmethods Ġin Ġrecommendation Ġsystems </s> There Ġare Ġdifferences Ġbetween Ġrecommendation Ġsystems Ġbased Ġon ĠL DA Ġand Ġtraditional Ġcollaborative Ġfiltering Ġ( CF ), Ġwe Ġdisc us Ġabout Ġthe Ġissue Ġof Ġ' Cold - start ', Ġ' lat ent Ġuser Ġinterest ' Ġand ĠSp arsity Ġin Ġthe Ġfield Ġof Ġrecomm ender Ġsystems . ĠIt Ġshould Ġbe Ġnoted Ġthat Ġto Ġovercome Ġthe Ġmajor Ġweaknesses Ġof ĠCF - based Ġrecommendation Ġsystems , Ġmany Ġmodels Ġhave Ġbeen Ġproposed , Ġsuch Ġas ĠB IB REF 47 . </s> Recommend ation Ġsystems Ġbased Ġon ĠL DA Ġin Ġcold - start , Ġthe Ġcold Ġstart Ġproblem Ġoccurs Ġwhen Ġa Ġnew Ġitem Ġor Ġuser Ġhas Ġjust Ġlogged Ġinto Ġthat Ġsystem ; Ġit Ġis Ġdifficult Ġto Ġfind Ġsimilar Ġones Ġbecause Ġthere Ġis Ġnot Ġenough Ġinformation . ĠL DA Ġcan Ġbe Ġeffective Ġand Ġuseful Ġto Ġdeal Ġwith Ġcold - start Ġin Ġrecommendation Ġsystems . ĠThere Ġare Ġapproaches Ġbased Ġon ĠL DA Ġto Ġdeal Ġwith Ġthis Ġproblem Ġthat Ġsome Ġof Ġthe Ġmethods Ġare Ġcombined Ġwith ĠCF Ġmethods , Ġfor Ġexample , ĠLin Ġet Ġal , Ġinvestigated Ġthe Ġcold - start Ġissue Ġwith Ġuse Ġthe Ġsocial Ġinformation Ġfor Ġapp Ġrecommendation Ġin ĠTwitter Ġand Ġused Ġa ĠL DA Ġmodel Ġto Ġdiscovering Ġlatent Ġgroup Ġfrom Ġ' Twitter Ġpersonalities ' Ġto Ġrecommendations Ġdiscovery Ġand Ġshown Ġthat Ġtheir Ġapproach Ġover comes Ġthe Ġdifficulty Ġof Ġcold - start Ġapp Ġrecommendation . ĠAlso ĠSome Ġresearchers Ġinvestigated Ġthe Ġcold - start Ġproblem Ġin Ġtag Ġrecommendation , Ġfor Ġexample ; ĠIn ĠB IB REF 60 Ġ, Ġpresented Ġa Ġsystem Ġrecommendation Ġbased Ġon ĠL DA Ġfor ĠCold - Start Ġin ĠMusic ĠRecommend ation Ġand Ġshowed Ġthat Ġtheir Ġapproach Ġcan Ġbe Ġuseful Ġin Ġhandling Ġthe Ġcold Ġstart Ġproblem Ġwhere Ġa Ġnew Ġsong Ġhasn 't Ġoccurred Ġin Ġthe Ġtraining Ġdata . ĠOther Ġresearchers Ġalso Ġanalyze Ġthe Ġcold Ġproblem Ġin Ġthe ĠVideo Ġrecommendation , Ġfor Ġexample ; ĠIn ĠB IB REF 52 Ġintroduced Ġa Ġunified ĠYouTube Ġvideo Ġrecommendation Ġsolution Ġvia Ġcross - network Ġcollaboration Ġand ĠL DA Ġto Ġaddress Ġthe Ġtypical Ġcold - start Ġand Ġdata Ġsp arsity Ġproblems Ġin Ġrecomm ender Ġsystems Ġand Ġshow Ġthat Ġthis Ġapproach Ġcan Ġbe Ġeffective Ġin Ġterms Ġof Ġprecision Ġand Ġimproving Ġthe Ġdiversity Ġof Ġthe Ġrecommended Ġvideos . </s> Recommend ation ĠSystems Ġbased Ġon ĠL DA Ġfor Ġ' Sp arsity Ġproblem ', Ġthe Ġdata Ġsp arsity Ġchallenge Ġhappens Ġwhen Ġthe Ġratio Ġis Ġtoo Ġsmall Ġto Ġsupply Ġenough Ġinformation Ġfor Ġeffective Ġpredictions Ġin ĠCF Ġsystems , Ġand Ġhence Ġthe Ġaccess Ġmatrix Ġis Ġvery Ġsparse . ĠRecommend ation Ġsystems Ġbased Ġon ĠL DA Ġwith Ġratings Ġdata Ġcan Ġprovide Ġsignificant Ġadvantage Ġand Ġaddition Ġcan Ġbe Ġuseful Ġfor Ġexplor atory Ġdata Ġanalysis Ġand Ġdimension ality Ġreduction Ġin Ġhuge Ġcontent - text . ĠThis Ġdimension ality Ġreduction Ġcan Ġalso Ġhelp Ġto Ġalleviate Ġthe Ġsp ars eness Ġproblem Ġwhich Ġis Ġinherent Ġto Ġmany Ġtraditional Ġcollaborative - fil tering Ġsystems . ĠThere Ġare Ġapproaches Ġbased Ġon ĠL DA Ġto Ġdeal Ġwith Ġthis ĠSc arcity Ġproblem Ġsuch Ġas ĠB IB REF 76 </s> Recommend ation ĠSystems Ġbased Ġon ĠL DA Ġfor Ġ' user Ġlatent Ġinterests ', Ġlatent Ġinterest Ġrefers Ġto Ġlong - term Ġinterest Ġin Ġa Ġspecific Ġtopic , Ġin Ġfact ; ĠA Ġlatent Ġinterest Ġcan Ġbe Ġviewed Ġas Ġone Ġspecific Ġcharacteristic Ġof Ġthe Ġusers Ġand Ġthe Ġitems Ġwho Ġhave Ġthis Ġlatent Ġinterest Ġwill Ġprefer Ġthe Ġitems Ġwith Ġthis Ġcharacteristic . ĠFinding Ġa Ġcommunity Ġwith Ġa Ġlatent Ġinterest Ġin Ġanother Ġcould Ġhelp Ġin Ġrecommending Ġinteresting Ġnew Ġcommunities Ġfor Ġa Ġuser . ĠHowever , Ġfor ĠCF Ġsystems , Ġit Ġis Ġhard Ġto Ġidentify Ġthe Ġuser Ġlatent Ġinterests , Ġsince Ġthe Ġonly Ġinformation Ġavailable Ġis Ġthe Ġuser Ġinteraction Ġinformation Ġwith Ġthe Ġsystem . ĠWhile Ġtopic Ġmodels Ġcan Ġbe Ġutilized Ġto Ġsimulate Ġthe Ġuser Ġlatent Ġinterests Ġand Ġshowed Ġthe Ġway Ġof Ġextracting Ġthese Ġinterests Ġfrom Ġthe Ġlatent ĠDir ich let Ġallocation Ġ( L DA ) Ġmodel Ġby Ġthe ĠGibbs Ġsampling Ġmethod Ġin Ġour Ġexperiment . ĠWe Ġshould Ġnote Ġthat , Ġin Ġterms Ġof Ġitems , Ġa Ġlatent Ġinterest Ġcan Ġbe Ġviewed Ġas Ġone Ġspecific Ġcharacteristic Ġof Ġthe Ġitems Ġand Ġthe Ġusers Ġwho Ġhave Ġthis Ġlatent Ġinterest Ġwill Ġprefer Ġthe Ġitems Ġwith Ġthis Ġcharacteristic . ĠFortunately , Ġas Ġa Ġsimulation Ġtool , Ġthe Ġtopic Ġmodel Ġ( e . g ., ĠL DA ) Ġcan Ġbe Ġutilized Ġto Ġlearn Ġthe Ġmeaning , Ġsignificance , Ġcharacteristics Ġand Ġattributes Ġof Ġitems Ġin Ġa Ġdata - driven , Ġi . e ., Ġfrom Ġgiven Ġrating Ġrecords , Ġpossibly Ġwithout Ġfurther Ġcontent Ġor Ġprior Ġknowledge Ġof Ġthese Ġitems BI B REF 77 . </s> Conclusion </s> In Ġthis Ġpaper , Ġwe Ġpresented Ġa Ġtax onomy Ġof Ġrecommendation Ġsystems Ġand Ġapplications Ġbased Ġon ĠL DA Ġof Ġthe Ġrecent Ġresearch , Ġincluding Ġapp , Ġtravel , Ġfriend , Ġlocation , Ġscientific Ġpaper , Ġand Ġmusic Ġrecommendation . ĠFurthermore , Ġwe Ġapplied ĠL DA Ġalgorithm Ġand ĠGibbs Ġsampling Ġon ĠIS WC Ġand ĠWW W Ġconference 's Ġpublications Ġfrom Ġ2013 - 2017 . ĠGenerally , Ġrecommendation Ġsystems Ġcan Ġbe Ġan Ġimpressive Ġinterface Ġbetween Ġonline Ġusers Ġand Ġwebsites Ġin Ġthe ĠInternet Ġcommunities . ĠOur Ġstudy Ġsuggest Ġthat ĠN LP Ġmethods Ġbased Ġon ĠL DA Ġcan Ġdiscover Ġhidden Ġaspects Ġto Ġbetter Ġunderstanding Ġof Ġthe Ġbehaviors Ġof Ġthe Ġpeople Ġto Ġbuild Ġsmart Ġrecommendation Ġsystems Ġin Ġonline Ġcommunities .
Introduction Today, Internet is one of the widest available media worldwide. It has essentially become a huge hit of data that has the potential to serve many information centric applications in our life. Recommendation system takes an essential part of many internet services and online applications, including applications like social-networking and recommendation of products (films, music, articles,..i.e.). Recommendation techniques have been used by the most known companies such as Amazon, Netflix and eBay to recommend releated items or products by estimating the probable preferences of customers. These techniques are profitable to both service provider and user. According to pervious works, two popular approaches for building recommendation systems can be categorized as content-based (CB), collaborative filtering (CF). Content-based (CB) recommending is adopted for recommendation systems model widely, which takes advantage of the property of items to create features and characteristics to coordinate user profiles. It can be relied on the properties of the items that each user likes to discover what else the user may like. One major issue of CB filtering method is that the recommendation system is required to gain an understanding of user preferences for some sorts of items and deploy these for other sorts of items. Nevertheless CF has two widely known problems which are sparsity and cold start (CS). In the rating matrix, The percentage of elements which get values is small. Even it is possible that CF considers only a few rating for popular items. For instance, upon a considerable Netflix rating dataset which is provided for Netflix Prize competition, there are about 100 milion ratings for about 18,000 movies that are given by 480,000 users. The percentage of rating matrix elements which are received ratings is 1. With a sparse ranking matrix it is very challenging topic to make an effective recommendation, depending on estimation of the relationship between items and users. CS problem is another widely known issue for CF approach, which can occur on new users or items. In terms of achieving an effective recommendation, CF approach requires either ratings on an item or a large number of ratings from a user. Recently, researchers proposed various methods based on probabilistic topic modeling methods BIBREF0. LDA is a generative probabilistic model broadly used in the information retrieval field. Researchers have used topic modeling methods based on LDA for building recommendation systems in various subjects, including app recommendation BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, event recommendationBIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, hashtag recommendation BIBREF16, BIBREF17, BIBREF18, BIBREF19, BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24, BIBREF25, BIBREF26, BIBREF27, BIBREF28, BIBREF29, BIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34, BIBREF35, BIBREF36, social networks and media BIBREF37, BIBREF38, BIBREF6, BIBREF9, BIBREF39, BIBREF40, BIBREF41. In this paper, We present a taxonomy of recommendation systems applications based on topic modeling (LDA) of the recent research and evaluate ISWC and WWW publications in computer science between 2013 to 2017 from DBLP dataset. Natural language Processing and LDA topic model Topic models are a powerful and practical tool for analyzing huge text documents in Natural language processing. Topic models can automatically cluster words into topics and discover relationship between documents from a dataset. For example; we can assume a three-topic model of a News Dataset, including "sport", "money" and “politic”. The most common words in the sport topic (Topic 1) might be “gym”, “football”, and “tennis” and in addition, for politic topic (Topic 2) might be “senator”, “president”, and “election”; while the money topic (Topic 3) can be made up of words such as “dollar”, “currency”, and “euro”. Figure 1; show a simple example for understanding a topic discovery from group of words. LDA is a popular technique to semantic analysis in topic modeling and text mining. LDA can apply in a diversity of text-information to evaluate topic trends over time and analyze large numbers of documents. In-process detail for LDA, defined a corpus (text) as $D={d_{1},d_{2},d_{3},....,d_{M}}$ where M is number of text documents and $d_{M}$ is a number of text documents in the corpus. A document is a series of N words denoted by $W={w_{1},w_{2},w_{3},....,w_{N}}$ , where $w_{N}$ is the $Nth$ word in the sequence of text document. In addition, z is a latent variable representing the hidden topic associated with each showed word. The generative procedure for LDA, formally defined as: For topic index $k\in \left\lbrace 1,2,...,k\right\rbrace $ i. Selected a word distribution $\beta _{k}~Dir\left(\eta \right)$ For text document $ D\in \left\lbrace 1,2,...,d\right\rbrace $ i.Selected a topic distribution $\_{k}~Dir\left(\eta \right)$ ii.For $n\in \left\lbrace 1,....,N_{d}\right\rbrace $ word a.Selected a topic assignment $z_{dn}~Mult\left(\theta _{d}\right)$ b.Selected a word $w_{dn}~Mult\left(\beta _{z_{d,n}}\right)$ Mult() is a multinomial distribution, and $Dir()$ is a Dirichlet distribution which is a prior distribution of Mult(),$\alpha $ and $\theta $ are hyperparameters. Natural language Processing and LDA topic model ::: Gibbs sampling and Learning LDA As previously mentioned, Topic modeling can find a collection of distributions over words for each topic and the relationship of topics with each document. To perform approximate inference and learning LDA, there are many inference methods for LDA topic model such as Gibbs sampling, collapsed Variational Bayes, Expectation Maximization. Gibbs sampling is a popular technique because of its simplicity and low latency. However, for large numbers of topics, Gibbs sampling can become unwieldy. In this paper, we use Gibbs Sampling in our experiment in section 5. Topic model based on recommendation systems: Recent research In this section, we considered six recommendation systems based on LDA which includes: scientific paper recommendation BIBREF42, BIBREF43, BIBREF44, BIBREF45, BIBREF46, BIBREF47, BIBREF48, BIBREF49, music and video recommendation BIBREF50, BIBREF51, BIBREF52, BIBREF53, BIBREF54, BIBREF55, BIBREF56, BIBREF57, BIBREF58, BIBREF59, BIBREF60, location recommendationBIBREF61, BIBREF37, BIBREF62, BIBREF63, BIBREF64, BIBREF65, BIBREF66, travel and tour recommendationBIBREF67, BIBREF38, BIBREF68, BIBREF69, app recommendationBIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, friend recommendation BIBREF41, BIBREF70, BIBREF71, as shown in Figure 2. Topic model based on recommendation systems: Recent research ::: Topic model based on scientific paper recommendation In recent years a considerable amount of research has addressed the task of defining models and systems for scientific papers recommendation; this trend has emerged as a natural consequence of the increasing growth of the number of scientific publications. For example, Youn and et al, proposed an approach to scientific articles'recommendation of user's interests based on a topic modeling framework. The authors, used a LDA model in order to extract the topics of the followees'tweets (followed Twitterers) and the paper titlesBIBREF49. They apply the Twitter-LDA algorithm simultaneously on the followees' tweets and the paper titles with the number of topics set to 200, they utilized the intersection of topics found in both paper titles and followees's tweets. Each followee of a user is ranked as follows: where $T_f$denotes all tweets by a followee, ${Topics}_p,\ $denotes the set of topics defining the titles of scientific articles, ${Topics}_f,\ $denotes the set of topics defining the tweets of a followee and $n\left(t,T_f\right)\ $the number of times a particular topic `t' from within occurs among the tweets of a followee. Based on the ranking scores of all followees of a particular user,and obtained top-k researchers followed by a target user. For evaluation approach, considered DBLP database as a large academic bibliographic network. Also, some researchers, introduced a combined model based on traditional collaborative filtering and topic modeling and designed a novel algorithm to scientific articles recommendation for users from an online community, called CTR model. They considered LDA to initialize the CTR model, Infact they combined the matrix factorization and the LDA model, and is shown their approach better than the recommendations based on matrix factorization. For evaluation and test, Used a large dataset from a bibliography sharing service (CiteULike) BIBREF47. Table 1, shown some impressive work based on LDA for paper recommendation. Topic model based on recommendation systems: Recent research ::: Topic model based on music and video recommendation Video and music recommendation has become an essential way for helping people explore the video world and discover the ones that may be of interest to them. Recently, analyze user interests and a good video or music recommendation in internet society is a big challenge. Hariri and et al. proposed a combined approach based on content and collaborative filtering methods from the sequence of songs listened to generate music recommendation. They applied a LDA model to reduce the dimensionality of the feature and obtain the hidden relationships between songs and tags. They collected 218,261 distinct songs from "Art of the Mix" website for evaluation their approach BIBREF60. Yan and et al, focused on the efficiency of users' information content on the online social network and provided a solution as a personalized video recommendation with considering users' cross-network social and content data. They applied a topic model based on LDA for each user, that user as document and user's hashtags as word, with considering user's information from Twitter BIBREF52. They derived Twitter user topic distributions and observed user-video interactions on YouTube, and presented a solution for user preference transfer: $u^{T_t}_i$, Twitter user tweet topic distribution matrix $u^{T_t}$ = $\lbrace $$u^{T_t}_i$ ;...;$\ u^{T_t}_{|U|}$ $\rbrace $, $u^{T_f}_i,$ Twitter user social topic distribution matrix $u^{T_f}$= $\lbrace $$u^{T_f}_i$ ;...;$\ u^{T_f}_{|U|}$ $\rbrace $, $W_1$and $W_2,\ $with observations of the overlapped user's Twitter and YouTube behaviors, $\mathrm {\Omega }$ as the collection of all the observed user-video pairs, $\eta $ is a trade-off parameter to balance the contribution of different types of user's behaviors on Twitter, where $v_j$ is the $j^{th}$row of $V$ , $L_j$ is the $j^{th}$column of L, $L_{jj}$ is the entry located in the $j^{th}$column and $j^{th}$ row of L. Based on, updated $v_j$ and $W_1$, $W_2$ iteratively until convergence or maximum iteration. The update rules are: where $\gamma $ denotes the learning rate. With the derived transfer matrices $W_1$, $W_2$ and video latent factor representations V, given a test user $u_i$ with his/her tweeting activity, friend collection, and the corresponding Twitter topical distributions $u^{T_t}_i$ , $u^{f_i}_i$ , we can estimate $u_i$ preferences on YouTube videos as: For test and experiment, utilized their approach on YouTube-Twitter dataset that include 9,253,729 tweeting behaviors and 1,097,982 video-related behavior and showed combining auxiliary network information and utilizing a cross-network Collaborative can lead to generating novel recommendations and increasing satisfaction for users. In addition, some researchers; proposed an approach based on Collaborative Filter (CF) Algorithm and utilized the application of session variety and temporal context. They applied a LDA model to temporal properties extraction of sessions and that considering sessions as documents and songs as words. For evaluation of this approach, they used Last.fm dataset(log) that includes 19,150,868 entries from 992 users. Results showed that the approach with using temporal information can increase the accuracy of music recommendations BIBREF54. In addition, other researchers used a dynamic framework based on four aspects of user's preference (collaborative aspect, content aspect, popularity aspect, and randomization aspect) for movie recommendation. The authors applied a linear combination model to generate the final recommendation list BIBREF51. Hu et al, proposed a novel topic modeling to audio retrieval, called GaussianLDA. In general, in this approach it was assumed that each audio document includes various latent topics and each topic considered as a Gaussian distribution. They prepared 1214 audio documents (length: between 0.82 s to 1 min), that each audio document is related with a category in different subject that includes: bell, river, rain, laugh, dog, gun and so on. Their results showed that the GaussianLDA model significantly outperform the standard LDA topic modelBIBREF56. Table 2, shown some impressive work based on LDA for music and video recommendation. Topic model based on recommendation systems: Recent research ::: Topic model based on location recommendation Recommendation systems based on location can suggest a set of places that users may be interested in, based on their history and behavior analysis. LDA can also be used for location recommendation. Kurashima et al, proposed a novel topic model for recommending new locations to visit, called Geo Topic Model. This model can predict user's interest and the user's spatial area based on features of visited locations. They used Tabelog-based (tablelog.com) and Flickr-based real-location log data for evaluation of their approach. They found that this model can discover latent topics related to art, great views, nature, atmosphere, and construction and other from logs of visited places BIBREF63. Table 3, shown some impressive work based on LDA for location recommendation. Liu and et al, investigated the POI recommendation issue in LBSNs by mining textual information and proposed a 'Topic and Location-aware' probabilistic matrix factorization (TL-PMF) method for Point-of-Interest recommendation to discover personalized recommendations from favorite places BIBREF37. The distribution over the observed ratings as well as the textual information is: Where, ${r}_{ij}$ be the rating of user ${u}_{i}$ for ${POI}_{{c}_{j}}{C}_{j}$, ${U}_{i}$and ${C}_{i}$ are the user and POI latent feature space vector respectively, $\mathcal {N}\left(\mathrm {\textrm {-}}\right|,{\sigma }^{2})$ is a Gaussian distribution with mean and variance ${\sigma }^{2}$, ${I}_{ij}$ is the indicator function, Function $f(U_i,C_{j,{TL}_{ij}})$ is to approximate the rating of user $u_i$ for ${POI}_{cj}$. they analyzed the topic characteristics from POIs across various geographical areas. The experiments were conducted on a large real-world LBSN dataset; they analyzed the topic characteristics from POIs across various geographical areas. Topic model based on recommendation systems: Recent research ::: Topic model based on friend recommendation Friend recommendation is a popular method to help users to make new friends and discover interesting information. Friend recommendation is a relative challenging issue contrasted with group or item recommendations in online social networksBIBREF70, BIBREF41, BIBREF71. To address this challenge issue, a recent work in [79] proposed a friend recommendation based on LDA, which contains two stages: first step, they applied tag-user information to produce a possible friend list and then they created a topic model to demonstrate the relationship between user's friend making behaviour and image features. They applied experiments on the Flickr as a standard dataset and showed that their recommends friends more quickly than traditional methods. Topic model based on recommendation systems: Recent research ::: Topic model Based on travel and tour recommendation Definitely, recommendation systems can have a significant impact to build a smart travel recommendation. Many different techniques have recently been developed to support travel recommendation based on different kinds of data. For example, in BIBREF67, the authors proposed a novel generative probabilistic model named socoLDA with heterogeneous social influence to better capture users's travel interests. They introduced the framework of travel-package recommendation named socoTraveler, which applys socoLDA to show a user's travel interests in topic space and find similar users to produce recommendations with user-based collaborative filtering. In addition, in BIBREF68, the authors proposed a framework to suggest top-k tours with highest marks for a user by using the photos shared by other users in an online social network. Table 4, shown some impressive work based on LDA for travel and tour recommendation. Topic model based on recommendation systems: Recent research ::: Topic model based on app recommendation Currently, a wide range of recommendation approaches have been proposed and applied to recommend mobile apps. For example, In BIBREF3, proposed a novel probabilistic model, named Goal-oriented Exploratory Model (GEM), to combine the identification of exploratory behavior and mobile app recommendations into a unified framework. The authors, employed the idea of LDA to design a topic model to cluster items into goals and identify the personal distribution of goals for each user and developed an effective and efficient algorithm, which integrates Expectation-Maximization (EM) algorithm with collapsed Gibbs sampling for model learning. They collected a mobile app dataset from Qihoo 360 Mobile Assistant, an open mobile app platform in China for Android users. Lin et al, investigated the cold-start issue with using the social information for App recommendation in Twitter and used a LDA model to discovering latent group from "Twitter personalities" to recommendations discovery. This approach is based on a simple "averaging" method where the probability of how likely the target user will like the app is the expectation of how the Twitter-followers like the app. Given a set of Twitter-followers $T$, the probability that user $u$ likes app a is defined as follows: where T(a) is the set of possible Twitter-followers following app a, in which assume that: (i) Twitter-followers are examined once at a time to make a decision about whether an app is liked or disliked. (ii) when the Twitter follower is known, the judgement does not depend on the app any more, i.e., (iii) the fact that given a user and an app, there is no judgement involved, i.e., (iv) the fact that an app has a given Twitter follower is independent from the user, i.e., Equation (DISPLAY_FORM24) is then reduced to the estimation of two quantities: 1. The probability that user u likes app a given that app a has Twitter-follower t, i.e., $p(+|t,\ u)$, and 2. The probability of considering Twitter-follower t given app a, i.e., $p(t|a)$ $p(+|t,\ u)$ is straightforward to estimate as it can be rewritten as: where $p(+,\ t|u)$ and $p(-,\ t|u)$ are derived from LDA, which is the probability that Twitter-follower t occurs in an app that is liked (or disliked) by user u. For test and experiment, they considered Apple's iTunes App Store and Twitter as dataset. Experimental results show, their approach significantly better than other state-of-the-art recommendation techniques BIBREF74. In BIBREF4, the authors proposed an allocation-based probabilistic mechanism that considers multiple user-app factors to help users with app recommendations. This framework that can capture geographical influences on usage behaviors and effectively model user mobility patterns, which in turn affect app usage patterns. They used Gibbs sampling to approximately estimate and infer the parameters of LDA. The authors measure the similarity of two location blocks by the similarity of app usage pattern, which is calculated by Pearson's correlation similarity BIBREF75: Specifically, defined $c({\mu }_i,{\alpha }_j,l_x)$=1, if user ${\mu }_i$has launched app ${\alpha }_j$at location block $l_x$. Otherwise, $c({\mu }_i,{\alpha }_j,l_x)$=0. Therefore, the number of users who have launched ${\alpha }_j$ at location block $l_x$ is $I_{aj,l_x}=\sum _{{\mu }_i\in U}{c({\mu }_i,{\alpha }_j,l_x)}$. where $\overline{I_{l_x}}$ denotes average mobile app influences at location block $l_x$. To determine whether a location block $l_z$ belongs to a location $L$, They define the app usage pattern coefficient as $r_{l_x}$for geographical block $l_x$. The initial collaborative filter coefficient is the average mobile app influence. To decide whether location block $l_z$ belongs to geographical region $L$. Also, they calculate its collaborative filter coefficient as follows : where ${s}_{L}$ is the number of location blocks in geographical region $L$. If the value of $r_{l_z}$$>$$\ r_{th}$, where $r_{th}$ is a predefined threshold value, location block $l_z$will be included in $L$. Experiment: Semantic Discovery and researchers behavior analysis ::: Dataset We extracted ISWC and WWW conferences publications from DBLP website by only considering conferences for which data was available for years 2013-2017. In total, It should be noted that in these experiments, we considered abstracts and titles from each article. In this paper, we used MALLET (http://mallet.cs.umass.edu/) to implement the inference and obtain the topic models. In addition, our full dataset is available at https://github.com/JeloH/Dataset_DBLP. The most important goal of this experiment is discover the trends of the topics and find relationship between LDA topics and paper features and generate trust tags. Experiment: Semantic Discovery and researchers behavior analysis ::: Parameter Settings In this paper, all experiments were carried out on a machine running Windows 7 with CoreI3 and 4 GB memory. We learn a LDA model with 100 topics; $\alpha =0.01$, $\beta = 0.01$ and using Gibbs sampling as a parameter estimation. Related words for a topic are quite intuitive and comprehensive in the sense of supplying a semantic short of a specific research field. Experiment: Semantic Discovery and researchers behavior analysis ::: Semantic analysis and generate tags In this section, we provide the results and discovered topics of 100-topics for ISWC and WWW conferences. According to Table 6 , the following observations can be made: In ISWC conference, Topic 25 sounds considerably more generic and is consistent with 'Concept Representation' in general, and marked by representation, structure, investigate, detection, values, and concept. Also, we can see that from 20 generated words in Topic 20, some words are very related to each other in means such as documents, scientific, networks, web, health, kg and we found that this topic covers papers that propose models in 'Documents Analysis in health research'. In WWW conference, Topic 7, this is our question; the word of 'ad'! Is for 'Advertisement' or 'Ad Hoc Network'? As we can see, the word 'ad' can be related to 'Advertisement' or also to 'ad hoc network'. To answer this question, it is very easy to see that topic 7 reveals social, modeling, ranking, question, ad, browser. If only we consider the words 'browser', news, social, we can predict that this topic can be related to 'Advertisement' and this topic covers papers that propose methods in 'Question Answering and Social Media'. Discussion, Open Issues and Future Directions In this study, we focused on the LDA approaches to recommendation systems and given the importance of research, we have studied recent impressive articles on this subject and presented a taxonomy of recommendation systems based on LDA of the recent research. we evaluated ISWC and WWW conferences articles from DBLP website and used the Gibbs sampling algorithm as an evaluation parameter. We succeeded in discovering the relationship between LDA topics and paper features and also obtained the researchers' interest in research field. According to our studies, some issues require further research, which can be very effective and attractive for the future. Discussion, Open Issues and Future Directions ::: Topic modeling methods and traditional methods in recommendation systems There are differences between recommendation systems based on LDA and traditional collaborative filtering (CF), we discus about the issue of 'Cold-start', 'latent user interest' and Sparsity in the field of recommender systems. It should be noted that to overcome the major weaknesses of CF-based recommendation systems, many models have been proposed, such as BIBREF47. Recommendation systems based on LDA in cold-start, the cold start problem occurs when a new item or user has just logged into that system; it is difficult to find similar ones because there is not enough information. LDA can be effective and useful to deal with cold-start in recommendation systems. There are approaches based on LDA to deal with this problem that some of the methods are combined with CF methods, for example, Lin et al, investigated the cold-start issue with use the social information for app recommendation in Twitter and used a LDA model to discovering latent group from 'Twitter personalities' to recommendations discovery and shown that their approach overcomes the difficulty of cold-start app recommendation. Also Some researchers investigated the cold-start problem in tag recommendation, for example; In BIBREF60 , presented a system recommendation based on LDA for Cold-Start in Music Recommendation and showed that their approach can be useful in handling the cold start problem where a new song hasn't occurred in the training data. Other researchers also analyze the cold problem in the Video recommendation, for example; In BIBREF52 introduced a unified YouTube video recommendation solution via cross-network collaboration and LDA to address the typical cold-start and data sparsity problems in recommender systems and show that this approach can be effective in terms of precision and improving the diversity of the recommended videos. Recommendation Systems based on LDA for 'Sparsity problem', the data sparsity challenge happens when the ratio is too small to supply enough information for effective predictions in CF systems, and hence the access matrix is very sparse. Recommendation systems based on LDA with ratings data can provide significant advantage and addition can be useful for exploratory data analysis and dimensionality reduction in huge content-text. This dimensionality reduction can also help to alleviate the sparseness problem which is inherent to many traditional collaborative-filtering systems. There are approaches based on LDA to deal with this Scarcity problem such as BIBREF76 Recommendation Systems based on LDA for 'user latent interests', latent interest refers to long-term interest in a specific topic, in fact; A latent interest can be viewed as one specific characteristic of the users and the items who have this latent interest will prefer the items with this characteristic. Finding a community with a latent interest in another could help in recommending interesting new communities for a user. However, for CF systems, it is hard to identify the user latent interests, since the only information available is the user interaction information with the system. While topic models can be utilized to simulate the user latent interests and showed the way of extracting these interests from the latent Dirichlet allocation (LDA) model by the Gibbs sampling method in our experiment. We should note that, in terms of items, a latent interest can be viewed as one specific characteristic of the items and the users who have this latent interest will prefer the items with this characteristic. Fortunately, as a simulation tool, the topic model (e.g., LDA) can be utilized to learn the meaning, significance, characteristics and attributes of items in a data-driven, i.e., from given rating records, possibly without further content or prior knowledge of these itemsBIBREF77. Conclusion In this paper, we presented a taxonomy of recommendation systems and applications based on LDA of the recent research, including app, travel, friend, location, scientific paper, and music recommendation. Furthermore, we applied LDA algorithm and Gibbs sampling on ISWC and WWW conference's publications from 2013-2017. Generally, recommendation systems can be an impressive interface between online users and websites in the Internet communities. Our study suggest that NLP methods based on LDA can discover hidden aspects to better understanding of the behaviors of the people to build smart recommendation systems in online communities.
How they utilize LDA and Gibbs sampling to evaluate ISWC and WWW publications?
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How Ġthey Ġutilize ĠL DA Ġand ĠGibbs Ġsampling Ġto Ġevaluate ĠIS WC Ġand ĠWW W Ġpublications ?
the ĠL DA Ġapproaches Ġto Ġrecommendation Ġsystems Ġand Ġgiven Ġthe Ġimportance Ġof Ġresearch , Ġwe Ġhave Ġstudied Ġrecent Ġimpressive Ġarticles Ġon Ġthis Ġsubject Ġand Ġpresented Ġa Ġtax onomy Ġof Ġrecommendation Ġsystems Ġbased Ġon ĠL DA Ġof Ġthe Ġrecent Ġresearch , Ġwe Ġevaluated ĠIS WC Ġand ĠWW W Ġconferences Ġarticles Ġfrom ĠD BL P Ġwebsite Ġand Ġused Ġthe ĠGibbs Ġsampling Ġalgorithm Ġas Ġan Ġevaluation Ġparameter
<s> How Ġthey Ġutilize ĠL DA Ġand ĠGibbs Ġsampling Ġto Ġevaluate ĠIS WC Ġand ĠWW W Ġpublications ? </s> Introduction </s> Today , ĠInternet Ġis Ġone Ġof Ġthe Ġwidest Ġavailable Ġmedia Ġworldwide . ĠIt Ġhas Ġessentially Ġbecome Ġa Ġhuge Ġhit Ġof Ġdata Ġthat Ġhas Ġthe Ġpotential Ġto Ġserve Ġmany Ġinformation Ġcent ric Ġapplications Ġin Ġour Ġlife . ĠRecommend ation Ġsystem Ġtakes Ġan Ġessential Ġpart Ġof Ġmany Ġinternet Ġservices Ġand Ġonline Ġapplications , Ġincluding Ġapplications Ġlike Ġsocial - net working Ġand Ġrecommendation Ġof Ġproducts Ġ( fil ms , Ġmusic , Ġarticles , .. i . e .). ĠRecommend ation Ġtechniques Ġhave Ġbeen Ġused Ġby Ġthe Ġmost Ġknown Ġcompanies Ġsuch Ġas ĠAmazon , ĠNetflix Ġand ĠeBay Ġto Ġrecommend Ġrele ated Ġitems Ġor Ġproducts Ġby Ġestimating Ġthe Ġprobable Ġpreferences Ġof Ġcustomers . ĠThese Ġtechniques Ġare Ġprofitable Ġto Ġboth Ġservice Ġprovider Ġand Ġuser . ĠAccording Ġto Ġper vious Ġworks , Ġtwo Ġpopular Ġapproaches Ġfor Ġbuilding Ġrecommendation Ġsystems Ġcan Ġbe Ġcategorized Ġas Ġcontent - based Ġ( CB ), Ġcollaborative Ġfiltering Ġ( CF ). </s> Content - based Ġ( CB ) Ġrecommending Ġis Ġadopted Ġfor Ġrecommendation Ġsystems Ġmodel Ġwidely , Ġwhich Ġtakes Ġadvantage Ġof Ġthe Ġproperty Ġof Ġitems Ġto Ġcreate Ġfeatures Ġand Ġcharacteristics Ġto Ġcoordinate Ġuser Ġprofiles . ĠIt Ġcan Ġbe Ġrelied Ġon Ġthe Ġproperties Ġof Ġthe Ġitems Ġthat Ġeach Ġuser Ġlikes Ġto Ġdiscover Ġwhat Ġelse Ġthe Ġuser Ġmay Ġlike . ĠOne Ġmajor Ġissue Ġof ĠCB Ġfiltering Ġmethod Ġis Ġthat Ġthe Ġrecommendation Ġsystem Ġis Ġrequired Ġto Ġgain Ġan Ġunderstanding Ġof Ġuser Ġpreferences Ġfor Ġsome Ġsorts Ġof Ġitems Ġand Ġdeploy Ġthese Ġfor Ġother Ġsorts Ġof Ġitems . </s> Nevertheless ĠCF Ġhas Ġtwo Ġwidely Ġknown Ġproblems Ġwhich Ġare Ġsp arsity Ġand Ġcold Ġstart Ġ( CS ). ĠIn Ġthe Ġrating Ġmatrix , ĠThe Ġpercentage Ġof Ġelements Ġwhich Ġget Ġvalues Ġis Ġsmall . ĠEven Ġit Ġis Ġpossible Ġthat ĠCF Ġconsiders Ġonly Ġa Ġfew Ġrating Ġfor Ġpopular Ġitems . ĠFor Ġinstance , Ġupon Ġa Ġconsiderable ĠNetflix Ġrating Ġdataset Ġwhich Ġis Ġprovided Ġfor ĠNetflix ĠPrize Ġcompetition , Ġthere Ġare Ġabout Ġ100 Ġmil ion Ġratings Ġfor Ġabout Ġ18 , 000 Ġmovies Ġthat Ġare Ġgiven Ġby Ġ480 , 000 Ġusers . ĠThe Ġpercentage Ġof Ġrating Ġmatrix Ġelements Ġwhich Ġare Ġreceived Ġratings Ġis Ġ1 . ĠWith Ġa Ġsparse Ġranking Ġmatrix Ġit Ġis Ġvery Ġchallenging Ġtopic Ġto Ġmake Ġan Ġeffective Ġrecommendation , Ġdepending Ġon Ġestimation Ġof Ġthe Ġrelationship Ġbetween Ġitems Ġand Ġusers . ĠCS Ġproblem Ġis Ġanother Ġwidely Ġknown Ġissue Ġfor ĠCF Ġapproach , Ġwhich Ġcan Ġoccur Ġon Ġnew Ġusers Ġor Ġitems . ĠIn Ġterms Ġof Ġachieving Ġan Ġeffective Ġrecommendation , ĠCF Ġapproach Ġrequires Ġeither Ġratings Ġon Ġan Ġitem Ġor Ġa Ġlarge Ġnumber Ġof Ġratings Ġfrom Ġa Ġuser . </s> Recently , Ġresearchers Ġproposed Ġvarious Ġmethods Ġbased Ġon Ġprob abil istic Ġtopic Ġmodeling Ġmethods ĠB IB REF 0 . ĠL DA Ġis Ġa Ġgener ative Ġprob abil istic Ġmodel Ġbroadly Ġused Ġin Ġthe Ġinformation Ġretrieval Ġfield . ĠResearchers Ġhave Ġused Ġtopic Ġmodeling Ġmethods Ġbased Ġon ĠL DA Ġfor Ġbuilding Ġrecommendation Ġsystems Ġin Ġvarious Ġsubjects , Ġincluding Ġapp Ġrecommendation ĠB IB REF 1 , ĠB IB REF 2 , ĠB IB REF 3 , ĠB IB REF 4 , ĠB IB REF 5 , Ġevent Ġrecommendation BI B REF 6 , ĠB IB REF 7 , ĠB IB REF 8 , ĠB IB REF 9 , ĠB IB REF 10 , ĠB IB REF 11 , ĠB IB REF 12 , ĠB IB REF 13 , ĠB IB REF 14 , ĠB IB REF 15 , Ġhashtag Ġrecommendation ĠB IB REF 16 , ĠB IB REF 17 , ĠB IB REF 18 , ĠB IB REF 19 , ĠB IB REF 20 , ĠB IB REF 21 , ĠB IB REF 22 , ĠB IB REF 23 , ĠB IB REF 24 , ĠB IB REF 25 , ĠB IB REF 26 , ĠB IB REF 27 , ĠB IB REF 28 , ĠB IB REF 29 , ĠB IB REF 30 , ĠB IB REF 31 , ĠB IB REF 32 , ĠB IB REF 33 , ĠB IB REF 34 , ĠB IB REF 35 , ĠB IB REF 36 , Ġsocial Ġnetworks Ġand Ġmedia ĠB IB REF 37 , ĠB IB REF 38 , ĠB IB REF 6 , ĠB IB REF 9 , ĠB IB REF 39 , ĠB IB REF 40 , ĠB IB REF 41 . ĠIn Ġthis Ġpaper , ĠWe Ġpresent Ġa Ġtax onomy Ġof Ġrecommendation Ġsystems Ġapplications Ġbased Ġon Ġtopic Ġmodeling Ġ( L DA ) Ġof Ġthe Ġrecent Ġresearch Ġand Ġevaluate ĠIS WC Ġand ĠWW W Ġpublications Ġin Ġcomputer Ġscience Ġbetween Ġ2013 Ġto Ġ2017 Ġfrom ĠD BL P Ġdataset . </s> Natural Ġlanguage ĠProcessing Ġand ĠL DA Ġtopic Ġmodel </s> Topic Ġmodels Ġare Ġa Ġpowerful Ġand Ġpractical Ġtool Ġfor Ġanalyzing Ġhuge Ġtext Ġdocuments Ġin ĠNatural Ġlanguage Ġprocessing . ĠTopic Ġmodels Ġcan Ġautomatically Ġcluster Ġwords Ġinto Ġtopics Ġand Ġdiscover Ġrelationship Ġbetween Ġdocuments Ġfrom Ġa Ġdataset . ĠFor Ġexample ; Ġwe Ġcan Ġassume Ġa Ġthree - topic Ġmodel Ġof Ġa ĠNews ĠDat as et , Ġincluding Ġ" s port ", Ġ" money " Ġand ĠâĢ ľ polit ic âĢ Ŀ . ĠThe Ġmost Ġcommon Ġwords Ġin Ġthe Ġsport Ġtopic Ġ( Topic Ġ1 ) Ġmight Ġbe ĠâĢ ľ gy m âĢ Ŀ , ĠâĢ ľ football âĢ Ŀ , Ġand ĠâĢ ľ ten nis âĢ Ŀ Ġand Ġin Ġaddition , Ġfor Ġpolitic Ġtopic Ġ( Topic Ġ2 ) Ġmight Ġbe ĠâĢ ľ sen ator âĢ Ŀ , ĠâĢ ľ president âĢ Ŀ , Ġand ĠâĢ ľ election âĢ Ŀ ; Ġwhile Ġthe Ġmoney Ġtopic Ġ( Topic Ġ3 ) Ġcan Ġbe Ġmade Ġup Ġof Ġwords Ġsuch Ġas ĠâĢ ľ dollar âĢ Ŀ , ĠâĢ ľ currency âĢ Ŀ , Ġand ĠâĢ ľ euro âĢ Ŀ . ĠFigure Ġ1 ; Ġshow Ġa Ġsimple Ġexample Ġfor Ġunderstanding Ġa Ġtopic Ġdiscovery Ġfrom Ġgroup Ġof Ġwords . ĠL DA Ġis Ġa Ġpopular Ġtechnique Ġto Ġsemantic Ġanalysis Ġin Ġtopic Ġmodeling Ġand Ġtext Ġmining . ĠL DA Ġcan Ġapply Ġin Ġa Ġdiversity Ġof Ġtext - information Ġto Ġevaluate Ġtopic Ġtrends Ġover Ġtime Ġand Ġanalyze Ġlarge Ġnumbers Ġof Ġdocuments . </s> In - process Ġdetail Ġfor ĠL DA , Ġdefined Ġa Ġcorpus Ġ( text ) Ġas Ġ$ D ={ d _{ 1 }, d _{ 2 }, d _{ 3 }, .... , d _{ M }} $ Ġwhere ĠM Ġis Ġnumber Ġof Ġtext Ġdocuments Ġand Ġ$ d _{ M } $ Ġis Ġa Ġnumber Ġof Ġtext Ġdocuments Ġin Ġthe Ġcorpus . ĠA Ġdocument Ġis Ġa Ġseries Ġof ĠN Ġwords Ġden oted Ġby Ġ$ W ={ w _{ 1 }, w _{ 2 }, w _{ 3 }, .... , w _{ N }} $ Ġ, Ġwhere Ġ$ w _{ N } $ Ġis Ġthe Ġ$ N th $ Ġword Ġin Ġthe Ġsequence Ġof Ġtext Ġdocument . ĠIn Ġaddition , Ġz Ġis Ġa Ġlatent Ġvariable Ġrepresenting Ġthe Ġhidden Ġtopic Ġassociated Ġwith Ġeach Ġshowed Ġword . ĠThe Ġgener ative Ġprocedure Ġfor ĠL DA , Ġformally Ġdefined Ġas : </s> For Ġtopic Ġindex Ġ$ k \ in Ġ\ left \ l brace Ġ1 , 2 ,... , k \ right \ r brace Ġ$ Ġi . ĠSelected Ġa Ġword Ġdistribution Ġ$\ beta Ġ_ { k } ~ Dir \ left (\ eta Ġ\ right ) $ </s> For Ġtext Ġdocument Ġ$ ĠD \ in Ġ\ left \ l brace Ġ1 , 2 ,... , d \ right \ r brace Ġ$ Ġi . Se lected Ġa Ġtopic Ġdistribution Ġ$\ _{ k } ~ Dir \ left (\ eta Ġ\ right ) $ Ġii . For Ġ$ n \ in Ġ\ left \ l brace Ġ1 , .... , N _{ d }\ right \ r brace Ġ$ Ġword Ġa . Se lected Ġa Ġtopic Ġassignment Ġ$ z _{ dn } ~ Mult \ left (\ the ta Ġ_ { d }\ right ) $ Ġb . Se lected Ġa Ġword Ġ$ w _{ dn } ~ Mult \ left (\ beta Ġ_ { z _{ d , n }} \ right ) $ </s> Mult () Ġis Ġa Ġmult in omial Ġdistribution , Ġand Ġ$ Dir () $ Ġis Ġa ĠDir ich let Ġdistribution Ġwhich Ġis Ġa Ġprior Ġdistribution Ġof ĠMult (), $ \ alpha Ġ$ Ġand Ġ$\ the ta Ġ$ Ġare Ġhyper param eters . </s> Natural Ġlanguage ĠProcessing Ġand ĠL DA Ġtopic Ġmodel Ġ: :: ĠGibbs Ġsampling Ġand ĠLearning ĠL DA </s> As Ġpreviously Ġmentioned , ĠTopic Ġmodeling Ġcan Ġfind Ġa Ġcollection Ġof Ġdistributions Ġover Ġwords Ġfor Ġeach Ġtopic Ġand Ġthe Ġrelationship Ġof Ġtopics Ġwith Ġeach Ġdocument . ĠTo Ġperform Ġapproximate Ġinference Ġand Ġlearning ĠL DA , Ġthere Ġare Ġmany Ġinference Ġmethods Ġfor ĠL DA Ġtopic Ġmodel Ġsuch Ġas ĠGibbs Ġsampling , Ġcollapsed ĠVari ational ĠBay es , ĠExpect ation ĠMaxim ization . ĠGibbs Ġsampling Ġis Ġa Ġpopular Ġtechnique Ġbecause Ġof Ġits Ġsimplicity Ġand Ġlow Ġlatency . ĠHowever , Ġfor Ġlarge Ġnumbers Ġof Ġtopics , ĠGibbs Ġsampling Ġcan Ġbecome Ġunw ield y . ĠIn Ġthis Ġpaper , Ġwe Ġuse ĠGibbs ĠSam pling Ġin Ġour Ġexperiment Ġin Ġsection Ġ5 . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch </s> In Ġthis Ġsection , Ġwe Ġconsidered Ġsix Ġrecommendation Ġsystems Ġbased Ġon ĠL DA Ġwhich Ġincludes : Ġscientific Ġpaper Ġrecommendation ĠB IB REF 42 , ĠB IB REF 43 , ĠB IB REF 44 , ĠB IB REF 45 , ĠB IB REF 46 , ĠB IB REF 47 , ĠB IB REF 48 , ĠB IB REF 49 , Ġmusic Ġand Ġvideo Ġrecommendation ĠB IB REF 50 , ĠB IB REF 51 , ĠB IB REF 52 , ĠB IB REF 53 , ĠB IB REF 54 , ĠB IB REF 55 , ĠB IB REF 56 , ĠB IB REF 57 , ĠB IB REF 58 , ĠB IB REF 59 , ĠB IB REF 60 , Ġlocation Ġrecommendation BI B REF 61 , ĠB IB REF 37 , ĠB IB REF 62 , ĠB IB REF 63 , ĠB IB REF 64 , ĠB IB REF 65 , ĠB IB REF 66 , Ġtravel Ġand Ġtour Ġrecommendation BI B REF 67 , ĠB IB REF 38 , ĠB IB REF 68 , ĠB IB REF 69 , Ġapp Ġrecommendation BI B REF 1 , ĠB IB REF 2 , ĠB IB REF 3 , ĠB IB REF 4 , ĠB IB REF 5 , Ġfriend Ġrecommendation ĠB IB REF 41 , ĠB IB REF 70 , ĠB IB REF 71 , Ġas Ġshown Ġin ĠFigure Ġ2 . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġscientific Ġpaper Ġrecommendation </s> In Ġrecent Ġyears Ġa Ġconsiderable Ġamount Ġof Ġresearch Ġhas Ġaddressed Ġthe Ġtask Ġof Ġdefining Ġmodels Ġand Ġsystems Ġfor Ġscientific Ġpapers Ġrecommendation ; Ġthis Ġtrend Ġhas Ġemerged Ġas Ġa Ġnatural Ġconsequence Ġof Ġthe Ġincreasing Ġgrowth Ġof Ġthe Ġnumber Ġof Ġscientific Ġpublications . ĠFor Ġexample , ĠYou n Ġand Ġet Ġal , Ġproposed Ġan Ġapproach Ġto Ġscientific Ġarticles 're comm end ation Ġof Ġuser 's Ġinterests Ġbased Ġon Ġa Ġtopic Ġmodeling Ġframework . ĠThe Ġauthors , Ġused Ġa ĠL DA Ġmodel Ġin Ġorder Ġto Ġextract Ġthe Ġtopics Ġof Ġthe Ġfollow ees 't we ets Ġ( follow ed ĠTw itt erers ) Ġand Ġthe Ġpaper Ġtitles BI B REF 49 . ĠThey Ġapply Ġthe ĠTwitter - L DA Ġalgorithm Ġsimultaneously Ġon Ġthe Ġfollow ees ' Ġtweets Ġand Ġthe Ġpaper Ġtitles Ġwith Ġthe Ġnumber Ġof Ġtopics Ġset Ġto Ġ200 , Ġthey Ġutilized Ġthe Ġintersection Ġof Ġtopics Ġfound Ġin Ġboth Ġpaper Ġtitles Ġand Ġfollow ees 's Ġtweets . ĠEach Ġfollow ee Ġof Ġa Ġuser Ġis Ġranked Ġas Ġfollows : </s> where Ġ$ T _ f $ den otes Ġall Ġtweets Ġby Ġa Ġfollow ee , </s> ${ Topics } _ p , \ Ġ$ den otes Ġthe Ġset Ġof Ġtopics Ġdefining Ġthe Ġtitles Ġof Ġscientific Ġarticles , </s> ${ Topics } _ f , \ Ġ$ den otes Ġthe Ġset Ġof Ġtopics Ġdefining Ġthe Ġtweets Ġof Ġa Ġfollow ee Ġand Ġ$ n \ left ( t , T _ f \ right )\ Ġ$ the Ġnumber Ġof Ġtimes Ġa Ġparticular Ġtopic Ġ` t ' Ġfrom Ġwithin Ġoccurs Ġamong Ġthe Ġtweets Ġof Ġa Ġfollow ee . ĠBased Ġon Ġthe Ġranking Ġscores Ġof Ġall Ġfollow ees Ġof Ġa Ġparticular Ġuser , and Ġobtained Ġtop - k Ġresearchers Ġfollowed Ġby Ġa Ġtarget Ġuser . ĠFor Ġevaluation Ġapproach , Ġconsidered ĠD BL P Ġdatabase Ġas Ġa Ġlarge Ġacademic Ġb ibli ographic Ġnetwork . </s> Also , Ġsome Ġresearchers , Ġintroduced Ġa Ġcombined Ġmodel Ġbased Ġon Ġtraditional Ġcollaborative Ġfiltering Ġand Ġtopic Ġmodeling Ġand Ġdesigned Ġa Ġnovel Ġalgorithm Ġto Ġscientific Ġarticles Ġrecommendation Ġfor Ġusers Ġfrom Ġan Ġonline Ġcommunity , Ġcalled ĠCTR Ġmodel . ĠThey Ġconsidered ĠL DA Ġto Ġinitialize Ġthe ĠCTR Ġmodel , ĠInf act Ġthey Ġcombined Ġthe Ġmatrix Ġfactor ization Ġand Ġthe ĠL DA Ġmodel , Ġand Ġis Ġshown Ġtheir Ġapproach Ġbetter Ġthan Ġthe Ġrecommendations Ġbased Ġon Ġmatrix Ġfactor ization . ĠFor Ġevaluation Ġand Ġtest , ĠUsed Ġa Ġlarge Ġdataset Ġfrom Ġa Ġb ibliography Ġsharing Ġservice Ġ( C ite UL ike ) ĠB IB REF 47 . ĠTable Ġ1 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġpaper Ġrecommendation . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġmusic Ġand Ġvideo Ġrecommendation </s> Video Ġand Ġmusic Ġrecommendation Ġhas Ġbecome Ġan Ġessential Ġway Ġfor Ġhelping Ġpeople Ġexplore Ġthe Ġvideo Ġworld Ġand Ġdiscover Ġthe Ġones Ġthat Ġmay Ġbe Ġof Ġinterest Ġto Ġthem . ĠRecently , Ġanalyze Ġuser Ġinterests Ġand Ġa Ġgood Ġvideo Ġor Ġmusic Ġrecommendation Ġin Ġinternet Ġsociety Ġis Ġa Ġbig Ġchallenge . ĠHar iri Ġand Ġet Ġal . Ġproposed Ġa Ġcombined Ġapproach Ġbased Ġon Ġcontent Ġand Ġcollaborative Ġfiltering Ġmethods Ġfrom Ġthe Ġsequence Ġof Ġsongs Ġlistened Ġto Ġgenerate Ġmusic Ġrecommendation . ĠThey Ġapplied Ġa ĠL DA Ġmodel Ġto Ġreduce Ġthe Ġdimension ality Ġof Ġthe Ġfeature Ġand Ġobtain Ġthe Ġhidden Ġrelationships Ġbetween Ġsongs Ġand Ġtags . ĠThey Ġcollected Ġ218 , 261 Ġdistinct Ġsongs Ġfrom Ġ" Art Ġof Ġthe ĠMix " Ġwebsite Ġfor Ġevaluation Ġtheir Ġapproach ĠB IB REF 60 . ĠYan Ġand Ġet Ġal , Ġfocused Ġon Ġthe Ġefficiency Ġof Ġusers ' Ġinformation Ġcontent Ġon Ġthe Ġonline Ġsocial Ġnetwork Ġand Ġprovided Ġa Ġsolution Ġas Ġa Ġpersonalized Ġvideo Ġrecommendation Ġwith Ġconsidering Ġusers ' Ġcross - network Ġsocial Ġand Ġcontent Ġdata . ĠThey Ġapplied Ġa Ġtopic Ġmodel Ġbased Ġon ĠL DA Ġfor Ġeach Ġuser , Ġthat Ġuser Ġas Ġdocument Ġand Ġuser 's Ġhasht ags Ġas Ġword , Ġwith Ġconsidering Ġuser 's Ġinformation Ġfrom ĠTwitter ĠB IB REF 52 . ĠThey Ġderived ĠTwitter Ġuser Ġtopic Ġdistributions Ġand Ġobserved Ġuser - video Ġinteractions Ġon ĠYouTube , Ġand Ġpresented Ġa Ġsolution Ġfor Ġuser Ġpreference Ġtransfer : </s> $ u ^{ T _ t } _ i $, ĠTwitter Ġuser Ġtweet Ġtopic Ġdistribution Ġmatrix Ġ$ u ^{ T _ t } $ Ġ= Ġ$\ l brace Ġ$$ u ^{ T _ t } _ i $ Ġ; ... ; $ \ Ġu ^{ T _ t } _{ | U | } $ Ġ$\ r brace Ġ$ , Ġ$ u ^{ T _ f } _ i , $ ĠTwitter Ġuser Ġsocial Ġtopic Ġdistribution Ġmatrix Ġ$ u ^{ T _ f } $ = Ġ$\ l brace Ġ$$ u ^{ T _ f } _ i $ Ġ; ... ; $ \ Ġu ^{ T _ f } _{ | U | } $ Ġ$\ r brace Ġ$ , Ġ$ W _ 1 $ and Ġ$ W _ 2 , \ Ġ$ with Ġobservations Ġof Ġthe Ġoverl apped Ġuser 's ĠTwitter Ġand ĠYouTube Ġbehaviors , Ġ$\ math rm Ġ{\ O mega Ġ} $ Ġas Ġthe Ġcollection Ġof Ġall Ġthe Ġobserved Ġuser - video Ġpairs , Ġ$\ eta Ġ$ Ġis Ġa Ġtrade - off Ġparameter Ġto Ġbalance Ġthe Ġcontribution Ġof Ġdifferent Ġtypes Ġof Ġuser 's Ġbehaviors Ġon ĠTwitter , Ġwhere Ġ$ v _ j $ Ġis Ġthe Ġ$ j ^{ th } $ row Ġof Ġ$ V $ Ġ, Ġ$ L _ j $ Ġis Ġthe Ġ$ j ^{ th } $ column Ġof ĠL , Ġ$ L _{ jj } $ Ġis Ġthe Ġentry Ġlocated Ġin Ġthe Ġ$ j ^{ th } $ column Ġand Ġ$ j ^{ th } $ Ġrow Ġof ĠL . ĠBased Ġon , Ġupdated Ġ$ v _ j $ Ġand Ġ$ W _ 1 $, Ġ$ W _ 2 $ Ġiter atively Ġuntil Ġconvergence Ġor Ġmaximum Ġiteration . ĠThe Ġupdate Ġrules Ġare : </s> where Ġ$\ gam ma Ġ$ Ġdenotes Ġthe Ġlearning Ġrate . </s> With Ġthe Ġderived Ġtransfer Ġmat rices Ġ$ W _ 1 $, Ġ$ W _ 2 $ Ġand Ġvideo Ġlatent Ġfactor Ġrepresentations ĠV , Ġgiven Ġa Ġtest Ġuser Ġ$ u _ i $ Ġwith Ġhis / her Ġtweeting Ġactivity , Ġfriend Ġcollection , Ġand Ġthe Ġcorresponding ĠTwitter Ġtopical Ġdistributions Ġ$ u ^{ T _ t } _ i $ Ġ, Ġ$ u ^{ f _ i } _ i $ Ġ, Ġwe Ġcan Ġestimate Ġ$ u _ i $ Ġpreferences Ġon ĠYouTube Ġvideos Ġas : </s> For Ġtest Ġand Ġexperiment , Ġutilized Ġtheir Ġapproach Ġon ĠYouTube - Twitter Ġdataset Ġthat Ġinclude Ġ9 , 253 , 729 Ġtweeting Ġbehaviors Ġand Ġ1 , 09 7 , 98 2 Ġvideo - related Ġbehavior Ġand Ġshowed Ġcombining Ġauxiliary Ġnetwork Ġinformation Ġand Ġutilizing Ġa Ġcross - network ĠCollabor ative Ġcan Ġlead Ġto Ġgenerating Ġnovel Ġrecommendations Ġand Ġincreasing Ġsatisfaction Ġfor Ġusers . </s> In Ġaddition , Ġsome Ġresearchers ; Ġproposed Ġan Ġapproach Ġbased Ġon ĠCollabor ative ĠFilter Ġ( CF ) ĠAl gorithm Ġand Ġutilized Ġthe Ġapplication Ġof Ġsession Ġvariety Ġand Ġtemporal Ġcontext . ĠThey Ġapplied Ġa ĠL DA Ġmodel Ġto Ġtemporal Ġproperties Ġextraction Ġof Ġsessions Ġand Ġthat Ġconsidering Ġsessions Ġas Ġdocuments Ġand Ġsongs Ġas Ġwords . ĠFor Ġevaluation Ġof Ġthis Ġapproach , Ġthey Ġused ĠLast . fm Ġdataset ( log ) Ġthat Ġincludes Ġ19 , 150 , 8 68 Ġentries Ġfrom Ġ9 92 Ġusers . ĠResults Ġshowed Ġthat Ġthe Ġapproach Ġwith Ġusing Ġtemporal Ġinformation Ġcan Ġincrease Ġthe Ġaccuracy Ġof Ġmusic Ġrecommendations ĠB IB REF 54 . </s> In Ġaddition , Ġother Ġresearchers Ġused Ġa Ġdynamic Ġframework Ġbased Ġon Ġfour Ġaspects Ġof Ġuser 's Ġpreference Ġ( coll abor ative Ġaspect , Ġcontent Ġaspect , Ġpopularity Ġaspect , Ġand Ġrandom ization Ġaspect ) Ġfor Ġmovie Ġrecommendation . ĠThe Ġauthors Ġapplied Ġa Ġlinear Ġcombination Ġmodel Ġto Ġgenerate Ġthe Ġfinal Ġrecommendation Ġlist ĠB IB REF 51 . ĠHu Ġet Ġal , Ġproposed Ġa Ġnovel Ġtopic Ġmodeling Ġto Ġaudio Ġretrieval , Ġcalled ĠGa ussian L DA . ĠIn Ġgeneral , Ġin Ġthis Ġapproach Ġit Ġwas Ġassumed Ġthat Ġeach Ġaudio Ġdocument Ġincludes Ġvarious Ġlatent Ġtopics Ġand Ġeach Ġtopic Ġconsidered Ġas Ġa ĠGa ussian Ġdistribution . ĠThey Ġprepared Ġ12 14 Ġaudio Ġdocuments Ġ( length : Ġbetween Ġ0 . 82 Ġs Ġto Ġ1 Ġmin ), Ġthat Ġeach Ġaudio Ġdocument Ġis Ġrelated Ġwith Ġa Ġcategory Ġin Ġdifferent Ġsubject Ġthat Ġincludes : Ġbell , Ġriver , Ġrain , Ġlaugh , Ġdog , Ġgun Ġand Ġso Ġon . ĠTheir Ġresults Ġshowed Ġthat Ġthe ĠGa ussian L DA Ġmodel Ġsignificantly Ġoutper form Ġthe Ġstandard ĠL DA Ġtopic Ġmodel BI B REF 56 . ĠTable Ġ2 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġmusic Ġand Ġvideo Ġrecommendation . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġlocation Ġrecommendation </s> Recommend ation Ġsystems Ġbased Ġon Ġlocation Ġcan Ġsuggest Ġa Ġset Ġof Ġplaces Ġthat Ġusers Ġmay Ġbe Ġinterested Ġin , Ġbased Ġon Ġtheir Ġhistory Ġand Ġbehavior Ġanalysis . ĠL DA Ġcan Ġalso Ġbe Ġused Ġfor Ġlocation Ġrecommendation . ĠKur ash ima Ġet Ġal , Ġproposed Ġa Ġnovel Ġtopic Ġmodel Ġfor Ġrecommending Ġnew Ġlocations Ġto Ġvisit , Ġcalled ĠGeo ĠTopic ĠModel . ĠThis Ġmodel Ġcan Ġpredict Ġuser 's Ġinterest Ġand Ġthe Ġuser 's Ġspatial Ġarea Ġbased Ġon Ġfeatures Ġof Ġvisited Ġlocations . ĠThey Ġused ĠT abel og - based Ġ( table log . com ) Ġand ĠFlickr - based Ġreal - location Ġlog Ġdata Ġfor Ġevaluation Ġof Ġtheir Ġapproach . ĠThey Ġfound Ġthat Ġthis Ġmodel Ġcan Ġdiscover Ġlatent Ġtopics Ġrelated Ġto Ġart , Ġgreat Ġviews , Ġnature , Ġatmosphere , Ġand Ġconstruction Ġand Ġother Ġfrom Ġlogs Ġof Ġvisited Ġplaces ĠB IB REF 63 . ĠTable Ġ3 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġlocation Ġrecommendation . </s> L iu Ġand Ġet Ġal , Ġinvestigated Ġthe ĠPO I Ġrecommendation Ġissue Ġin ĠL BS Ns Ġby Ġmining Ġtextual Ġinformation Ġand Ġproposed Ġa Ġ' Topic Ġand ĠLocation - aware ' Ġprob abil istic Ġmatrix Ġfactor ization Ġ( TL - PM F ) Ġmethod Ġfor ĠPoint - of - Interest Ġrecommendation Ġto Ġdiscover Ġpersonalized Ġrecommendations Ġfrom Ġfavorite Ġplaces ĠB IB REF 37 . ĠThe Ġdistribution Ġover Ġthe Ġobserved Ġratings Ġas Ġwell Ġas Ġthe Ġtextual Ġinformation Ġis : </s> Where , Ġ${ r } _{ ij } $ Ġbe Ġthe Ġrating Ġof Ġuser Ġ${ u } _{ i } $ Ġfor Ġ${ PO I } _{ { c } _{ j }} { C } _{ j } $, </s> ${ U } _{ i } $ and Ġ${ C } _{ i } $ Ġare Ġthe Ġuser Ġand ĠPO I Ġlatent Ġfeature Ġspace Ġvector Ġrespectively , </s> $ \ math cal Ġ{ N }\ left (\ math rm Ġ{\ text rm Ġ{ - }} \ right | , {\ s igma Ġ} ^{ 2 }) $ Ġis Ġa ĠGa ussian Ġdistribution Ġwith Ġmean Ġand Ġvariance Ġ${ \ s igma Ġ} ^{ 2 } $, </s> ${ I } _{ ij } $ Ġis Ġthe Ġindicator Ġfunction , </s> Function Ġ$ f ( U _ i , C _{ j , { TL } _{ ij }} ) $ Ġis Ġto Ġapproximate Ġthe Ġrating Ġof Ġuser Ġ$ u _ i $ Ġfor Ġ${ PO I } _{ c j } $. </s> they Ġanalyzed Ġthe Ġtopic Ġcharacteristics Ġfrom ĠPO Is Ġacross Ġvarious Ġgeographical Ġareas . ĠThe Ġexperiments Ġwere Ġconducted Ġon Ġa Ġlarge Ġreal - world ĠL BS N Ġdataset ; Ġthey Ġanalyzed Ġthe Ġtopic Ġcharacteristics Ġfrom ĠPO Is Ġacross Ġvarious Ġgeographical Ġareas . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġfriend Ġrecommendation </s> Friend Ġrecommendation Ġis Ġa Ġpopular Ġmethod Ġto Ġhelp Ġusers Ġto Ġmake Ġnew Ġfriends Ġand Ġdiscover Ġinteresting Ġinformation . ĠFriend Ġrecommendation Ġis Ġa Ġrelative Ġchallenging Ġissue Ġcontrasted Ġwith Ġgroup Ġor Ġitem Ġrecommendations Ġin Ġonline Ġsocial Ġnetworks BI B REF 70 , ĠB IB REF 41 , ĠB IB REF 71 . ĠTo Ġaddress Ġthis Ġchallenge Ġissue , Ġa Ġrecent Ġwork Ġin Ġ[ 79 ] Ġproposed Ġa Ġfriend Ġrecommendation Ġbased Ġon ĠL DA , Ġwhich Ġcontains Ġtwo Ġstages : Ġfirst Ġstep , Ġthey Ġapplied Ġtag - user Ġinformation Ġto Ġproduce Ġa Ġpossible Ġfriend Ġlist Ġand Ġthen Ġthey Ġcreated Ġa Ġtopic Ġmodel Ġto Ġdemonstrate Ġthe Ġrelationship Ġbetween Ġuser 's Ġfriend Ġmaking Ġbehaviour Ġand Ġimage Ġfeatures . ĠThey Ġapplied Ġexperiments Ġon Ġthe ĠFlickr Ġas Ġa Ġstandard Ġdataset Ġand Ġshowed Ġthat Ġtheir Ġrecommends Ġfriends Ġmore Ġquickly Ġthan Ġtraditional Ġmethods . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel ĠBased Ġon Ġtravel Ġand Ġtour Ġrecommendation </s> Def initely , Ġrecommendation Ġsystems Ġcan Ġhave Ġa Ġsignificant Ġimpact Ġto Ġbuild Ġa Ġsmart Ġtravel Ġrecommendation . ĠMany Ġdifferent Ġtechniques Ġhave Ġrecently Ġbeen Ġdeveloped Ġto Ġsupport Ġtravel Ġrecommendation Ġbased Ġon Ġdifferent Ġkinds Ġof Ġdata . ĠFor Ġexample , Ġin ĠB IB REF 67 , Ġthe Ġauthors Ġproposed Ġa Ġnovel Ġgener ative Ġprob abil istic Ġmodel Ġnamed Ġsoc o L DA Ġwith Ġheter ogeneous Ġsocial Ġinfluence Ġto Ġbetter Ġcapture Ġusers 's Ġtravel Ġinterests . ĠThey Ġintroduced Ġthe Ġframework Ġof Ġtravel - package Ġrecommendation Ġnamed Ġsoc o Travel er , Ġwhich Ġapply s Ġsoc o L DA Ġto Ġshow Ġa Ġuser 's Ġtravel Ġinterests Ġin Ġtopic Ġspace Ġand Ġfind Ġsimilar Ġusers Ġto Ġproduce Ġrecommendations Ġwith Ġuser - based Ġcollaborative Ġfiltering . ĠIn Ġaddition , Ġin ĠB IB REF 68 , Ġthe Ġauthors Ġproposed Ġa Ġframework Ġto Ġsuggest Ġtop - k Ġtours Ġwith Ġhighest Ġmarks Ġfor Ġa Ġuser Ġby Ġusing Ġthe Ġphotos Ġshared Ġby Ġother Ġusers Ġin Ġan Ġonline Ġsocial Ġnetwork . ĠTable Ġ4 , Ġshown Ġsome Ġimpressive Ġwork Ġbased Ġon ĠL DA Ġfor Ġtravel Ġand Ġtour Ġrecommendation . </s> Topic Ġmodel Ġbased Ġon Ġrecommendation Ġsystems : ĠRecent Ġresearch Ġ: :: ĠTopic Ġmodel Ġbased Ġon Ġapp Ġrecommendation </s> Currently , Ġa Ġwide Ġrange Ġof Ġrecommendation Ġapproaches Ġhave Ġbeen Ġproposed Ġand Ġapplied Ġto Ġrecommend Ġmobile Ġapps . ĠFor Ġexample , ĠIn ĠB IB REF 3 , Ġproposed Ġa Ġnovel Ġprob abil istic Ġmodel , Ġnamed ĠGoal - oriented ĠExpl or atory ĠModel Ġ( G EM ), Ġto Ġcombine Ġthe Ġidentification Ġof Ġexplor atory Ġbehavior Ġand Ġmobile Ġapp Ġrecommendations Ġinto Ġa Ġunified Ġframework . ĠThe Ġauthors , Ġemployed Ġthe Ġidea Ġof ĠL DA Ġto Ġdesign Ġa Ġtopic Ġmodel Ġto Ġcluster Ġitems Ġinto Ġgoals Ġand Ġidentify Ġthe Ġpersonal Ġdistribution Ġof Ġgoals Ġfor Ġeach Ġuser Ġand Ġdeveloped Ġan Ġeffective Ġand Ġefficient Ġalgorithm , Ġwhich Ġintegrates ĠExpect ation - Max im ization Ġ( EM ) Ġalgorithm Ġwith Ġcollapsed ĠGibbs Ġsampling Ġfor Ġmodel Ġlearning . ĠThey Ġcollected Ġa Ġmobile Ġapp Ġdataset Ġfrom ĠQ ih oo Ġ360 ĠMobile ĠAssistant , Ġan Ġopen Ġmobile Ġapp Ġplatform Ġin ĠChina Ġfor ĠAndroid Ġusers . ĠLin Ġet Ġal , Ġinvestigated Ġthe Ġcold - start Ġissue Ġwith Ġusing Ġthe Ġsocial Ġinformation Ġfor ĠApp Ġrecommendation Ġin ĠTwitter Ġand Ġused Ġa ĠL DA Ġmodel Ġto Ġdiscovering Ġlatent Ġgroup Ġfrom Ġ" Twitter Ġpersonalities " Ġto Ġrecommendations Ġdiscovery . ĠThis Ġapproach Ġis Ġbased Ġon Ġa Ġsimple Ġ" aver aging " Ġmethod Ġwhere Ġthe Ġprobability Ġof Ġhow Ġlikely Ġthe Ġtarget Ġuser Ġwill Ġlike Ġthe Ġapp Ġis Ġthe Ġexpectation Ġof Ġhow Ġthe ĠTwitter - follow ers Ġlike Ġthe Ġapp . ĠGiven Ġa Ġset Ġof ĠTwitter - follow ers Ġ$ T $, Ġthe Ġprobability Ġthat Ġuser Ġ$ u $ Ġlikes Ġapp Ġa Ġis Ġdefined Ġas Ġfollows : </s> where ĠT ( a ) Ġis Ġthe Ġset Ġof Ġpossible ĠTwitter - follow ers Ġfollowing Ġapp Ġa , Ġin Ġwhich Ġassume Ġthat : </s> ( i ) ĠTwitter - follow ers Ġare Ġexamined Ġonce Ġat Ġa Ġtime Ġto Ġmake Ġa Ġdecision Ġabout Ġwhether Ġan Ġapp Ġis Ġliked Ġor Ġdisliked . </s> ( ii ) Ġwhen Ġthe ĠTwitter Ġfollower Ġis Ġknown , Ġthe Ġjudgement Ġdoes Ġnot Ġdepend Ġon Ġthe Ġapp Ġany Ġmore , Ġi . e ., </s> ( iii ) Ġthe Ġfact Ġthat Ġgiven Ġa Ġuser Ġand Ġan Ġapp , Ġthere Ġis Ġno Ġjudgement Ġinvolved , Ġi . e ., </s> ( iv ) Ġthe Ġfact Ġthat Ġan Ġapp Ġhas Ġa Ġgiven ĠTwitter Ġfollower Ġis Ġindependent Ġfrom Ġthe Ġuser , Ġi . e ., </s> Equ ation Ġ( DIS PLAY _ FORM 24 ) Ġis Ġthen Ġreduced Ġto Ġthe Ġestimation Ġof Ġtwo Ġquantities : </s> 1 . ĠThe Ġprobability Ġthat Ġuser Ġu Ġlikes Ġapp Ġa Ġgiven Ġthat Ġapp Ġa Ġhas ĠTwitter - f ollower Ġt , Ġi . e ., Ġ$ p ( + | t , \ Ġu ) $, Ġand </s> 2 . ĠThe Ġprobability Ġof Ġconsidering ĠTwitter - f ollower Ġt Ġgiven Ġapp Ġa , Ġi . e ., Ġ$ p ( t | a ) $ </s> $ p ( + | t , \ Ġu ) $ Ġis Ġstraightforward Ġto Ġestimate Ġas Ġit Ġcan Ġbe Ġrewritten Ġas : </s> where Ġ$ p ( +, \ Ġt | u ) $ Ġand Ġ$ p ( -, \ Ġt | u ) $ Ġare Ġderived Ġfrom ĠL DA , Ġwhich Ġis Ġthe Ġprobability Ġthat ĠTwitter - f ollower Ġt Ġoccurs Ġin Ġan Ġapp Ġthat Ġis Ġliked Ġ( or Ġdisliked ) Ġby Ġuser Ġu . </s> For Ġtest Ġand Ġexperiment , Ġthey Ġconsidered ĠApple 's ĠiTunes ĠApp ĠStore Ġand ĠTwitter Ġas Ġdataset . ĠExperimental Ġresults Ġshow , Ġtheir Ġapproach Ġsignificantly Ġbetter Ġthan Ġother Ġstate - of - the - art Ġrecommendation Ġtechniques ĠB IB REF 74 . </s> In ĠB IB REF 4 , Ġthe Ġauthors Ġproposed Ġan Ġallocation - based Ġprob abil istic Ġmechanism Ġthat Ġconsiders Ġmultiple Ġuser - app Ġfactors Ġto Ġhelp Ġusers Ġwith Ġapp Ġrecommendations . ĠThis Ġframework Ġthat Ġcan Ġcapture Ġgeographical Ġinfluences Ġon Ġusage Ġbehaviors Ġand Ġeffectively Ġmodel Ġuser Ġmobility Ġpatterns , Ġwhich Ġin Ġturn Ġaffect Ġapp Ġusage Ġpatterns . ĠThey Ġused ĠGibbs Ġsampling Ġto Ġapproximately Ġestimate Ġand Ġinfer Ġthe Ġparameters Ġof ĠL DA . ĠThe Ġauthors Ġmeasure Ġthe Ġsimilarity Ġof Ġtwo Ġlocation Ġblocks Ġby Ġthe Ġsimilarity Ġof Ġapp Ġusage Ġpattern , Ġwhich Ġis Ġcalculated Ġby ĠPearson 's Ġcorrelation Ġsimilarity ĠB IB REF 75 : </s> Specifically , </s> defined Ġ$ c ({ \ mu Ġ} _ i , {\ alpha Ġ} _ j , l _ x ) $ = 1 , Ġif Ġuser Ġ${ \ mu Ġ} _ i $ has Ġlaunched Ġapp Ġ${ \ alpha Ġ} _ j $ at Ġlocation Ġblock Ġ$ l _ x $. </s> Otherwise , Ġ$ c ({ \ mu Ġ} _ i , {\ alpha Ġ} _ j , l _ x ) $ = 0 . ĠTherefore , Ġthe Ġnumber Ġof Ġusers Ġwho Ġhave Ġlaunched Ġ${ \ alpha Ġ} _ j $ Ġat Ġlocation Ġblock Ġ$ l _ x $ Ġis Ġ$ I _{ aj , l _ x } = \ sum Ġ_ {{ \ mu Ġ} _ i \ in ĠU }{ c ({ \ mu Ġ} _ i , {\ alpha Ġ} _ j , l _ x )} $. </s> where Ġ$\ over line { I _{ l _ x }} $ Ġdenotes Ġaverage Ġmobile Ġapp Ġinfluences Ġat Ġlocation Ġblock Ġ$ l _ x $. </s> To Ġdetermine Ġwhether Ġa Ġlocation Ġblock Ġ$ l _ z $ Ġbelongs Ġto Ġa Ġlocation Ġ$ L $, </s> They Ġdefine Ġthe Ġapp Ġusage Ġpattern Ġcoefficient Ġas Ġ$ r _{ l _ x } $ for Ġgeographical Ġblock Ġ$ l _ x $. ĠThe Ġinitial Ġcollaborative Ġfilter Ġcoefficient Ġis Ġthe Ġaverage Ġmobile Ġapp Ġinfluence . ĠTo Ġdecide Ġwhether Ġlocation Ġblock Ġ$ l _ z $ Ġbelongs Ġto Ġgeographical Ġregion Ġ$ L $. ĠAlso , Ġthey Ġcalculate Ġits Ġcollaborative Ġfilter Ġcoefficient Ġas Ġfollows Ġ: </s> where Ġ${ s } _{ L } $ Ġis Ġthe Ġnumber Ġof Ġlocation Ġblocks Ġin Ġgeographical Ġregion Ġ$ L $. ĠIf Ġthe Ġvalue Ġof Ġ$ r _{ l _ z } $$ > $$ \ Ġr _{ th } $, Ġwhere Ġ$ r _{ th } $ Ġis Ġa Ġpred efined Ġthreshold Ġvalue , Ġlocation Ġblock Ġ$ l _ z $ will Ġbe Ġincluded Ġin Ġ$ L $. </s> Exper iment : ĠSem antic ĠDiscovery Ġand Ġresearchers Ġbehavior Ġanalysis Ġ: :: ĠDat as et </s> We Ġextracted ĠIS WC Ġand ĠWW W Ġconferences Ġpublications Ġfrom ĠD BL P Ġwebsite Ġby Ġonly Ġconsidering Ġconferences Ġfor Ġwhich Ġdata Ġwas Ġavailable Ġfor Ġyears Ġ2013 - 2017 . ĠIn Ġtotal , ĠIt Ġshould Ġbe Ġnoted Ġthat Ġin Ġthese Ġexperiments , Ġwe Ġconsidered Ġabstract s Ġand Ġtitles Ġfrom Ġeach Ġarticle . ĠIn Ġthis Ġpaper , Ġwe Ġused ĠM AL LET Ġ( http :// mal let . cs . um ass . edu /) Ġto Ġimplement Ġthe Ġinference Ġand Ġobtain Ġthe Ġtopic Ġmodels . ĠIn Ġaddition , Ġour Ġfull Ġdataset Ġis Ġavailable Ġat Ġhttps :// github . com / J elo H / Dat as et _ D BL P . ĠThe Ġmost Ġimportant Ġgoal Ġof Ġthis Ġexperiment Ġis Ġdiscover Ġthe Ġtrends Ġof Ġthe Ġtopics Ġand Ġfind Ġrelationship Ġbetween ĠL DA Ġtopics Ġand Ġpaper Ġfeatures Ġand Ġgenerate Ġtrust Ġtags . </s> Exper iment : ĠSem antic ĠDiscovery Ġand Ġresearchers Ġbehavior Ġanalysis Ġ: :: ĠParam eter ĠSettings </s> In Ġthis Ġpaper , Ġall Ġexperiments Ġwere Ġcarried Ġout Ġon Ġa Ġmachine Ġrunning ĠWindows Ġ7 Ġwith ĠCore I 3 Ġand Ġ4 ĠGB Ġmemory . ĠWe Ġlearn Ġa ĠL DA Ġmodel Ġwith Ġ100 Ġtopics ; Ġ$\ alpha Ġ= 0 . 01 $, Ġ$\ beta Ġ= Ġ0 . 01 $ Ġand Ġusing ĠGibbs Ġsampling Ġas Ġa Ġparameter Ġestimation . ĠRelated Ġwords Ġfor Ġa Ġtopic Ġare Ġquite Ġintuitive Ġand Ġcomprehensive Ġin Ġthe Ġsense Ġof Ġsupplying Ġa Ġsemantic Ġshort Ġof Ġa Ġspecific Ġresearch Ġfield . </s> Exper iment : ĠSem antic ĠDiscovery Ġand Ġresearchers Ġbehavior Ġanalysis Ġ: :: ĠSem antic Ġanalysis Ġand Ġgenerate Ġtags </s> In Ġthis Ġsection , Ġwe Ġprovide Ġthe Ġresults Ġand Ġdiscovered Ġtopics Ġof Ġ100 - top ics Ġfor ĠIS WC Ġand ĠWW W Ġconferences . </s> According Ġto ĠTable Ġ6 Ġ, Ġthe Ġfollowing Ġobservations Ġcan Ġbe Ġmade : </s> In ĠIS WC Ġconference , ĠTopic Ġ25 Ġsounds Ġconsiderably Ġmore Ġgeneric Ġand Ġis Ġconsistent Ġwith Ġ' Con cept ĠRepresent ation ' Ġin Ġgeneral , Ġand Ġmarked Ġby Ġrepresentation , Ġstructure , Ġinvestigate , Ġdetection , Ġvalues , Ġand Ġconcept . ĠAlso , Ġwe Ġcan Ġsee Ġthat Ġfrom Ġ20 Ġgenerated Ġwords Ġin ĠTopic Ġ20 , Ġsome Ġwords Ġare Ġvery Ġrelated Ġto Ġeach Ġother Ġin Ġmeans Ġsuch Ġas Ġdocuments , Ġscientific , Ġnetworks , Ġweb , Ġhealth , Ġkg Ġand Ġwe Ġfound Ġthat Ġthis Ġtopic Ġcovers Ġpapers Ġthat Ġpropose Ġmodels Ġin Ġ' Documents ĠAnalysis Ġin Ġhealth Ġresearch '. </s> In ĠWW W Ġconference , ĠTopic Ġ7 , Ġthis Ġis Ġour Ġquestion ; Ġthe Ġword Ġof Ġ' ad ' ! ĠIs Ġfor Ġ' Advertisement ' Ġor Ġ' Ad ĠH oc ĠNetwork '? ĠAs Ġwe Ġcan Ġsee , Ġthe Ġword Ġ' ad ' Ġcan Ġbe Ġrelated Ġto Ġ' Advertisement ' Ġor Ġalso Ġto Ġ' ad Ġhoc Ġnetwork '. ĠTo Ġanswer Ġthis Ġquestion , Ġit Ġis Ġvery Ġeasy Ġto Ġsee Ġthat Ġtopic Ġ7 Ġreveals Ġsocial , Ġmodeling , Ġranking , Ġquestion , Ġad , Ġbrowser . ĠIf Ġonly Ġwe Ġconsider Ġthe Ġwords Ġ' browser ', Ġnews , Ġsocial , Ġwe Ġcan Ġpredict Ġthat Ġthis Ġtopic Ġcan Ġbe Ġrelated Ġto Ġ' Advertisement ' Ġand Ġthis Ġtopic Ġcovers Ġpapers Ġthat Ġpropose Ġmethods Ġin Ġ' Question ĠAn sw ering Ġand ĠSocial ĠMedia '. </s> Discussion , ĠOpen ĠIssues Ġand ĠFuture ĠDirections </s> In Ġthis Ġstudy , Ġwe Ġfocused Ġon Ġthe ĠL DA Ġapproaches Ġto Ġrecommendation Ġsystems Ġand Ġgiven Ġthe Ġimportance Ġof Ġresearch , Ġwe Ġhave Ġstudied Ġrecent Ġimpressive Ġarticles Ġon Ġthis Ġsubject Ġand Ġpresented Ġa Ġtax onomy Ġof Ġrecommendation Ġsystems Ġbased Ġon ĠL DA Ġof Ġthe Ġrecent Ġresearch . Ġwe Ġevaluated ĠIS WC Ġand ĠWW W Ġconferences Ġarticles Ġfrom ĠD BL P Ġwebsite Ġand Ġused Ġthe ĠGibbs Ġsampling Ġalgorithm Ġas Ġan Ġevaluation Ġparameter . ĠWe Ġsucceeded Ġin Ġdiscovering Ġthe Ġrelationship Ġbetween ĠL DA Ġtopics Ġand Ġpaper Ġfeatures Ġand Ġalso Ġobtained Ġthe Ġresearchers ' Ġinterest Ġin Ġresearch Ġfield . ĠAccording Ġto Ġour Ġstudies , Ġsome Ġissues Ġrequire Ġfurther Ġresearch , Ġwhich Ġcan Ġbe Ġvery Ġeffective Ġand Ġattractive Ġfor Ġthe Ġfuture . </s> Discussion , ĠOpen ĠIssues Ġand ĠFuture ĠDirections Ġ: :: ĠTopic Ġmodeling Ġmethods Ġand Ġtraditional Ġmethods Ġin Ġrecommendation Ġsystems </s> There Ġare Ġdifferences Ġbetween Ġrecommendation Ġsystems Ġbased Ġon ĠL DA Ġand Ġtraditional Ġcollaborative Ġfiltering Ġ( CF ), Ġwe Ġdisc us Ġabout Ġthe Ġissue Ġof Ġ' Cold - start ', Ġ' lat ent Ġuser Ġinterest ' Ġand ĠSp arsity Ġin Ġthe Ġfield Ġof Ġrecomm ender Ġsystems . ĠIt Ġshould Ġbe Ġnoted Ġthat Ġto Ġovercome Ġthe Ġmajor Ġweaknesses Ġof ĠCF - based Ġrecommendation Ġsystems , Ġmany Ġmodels Ġhave Ġbeen Ġproposed , Ġsuch Ġas ĠB IB REF 47 . </s> Recommend ation Ġsystems Ġbased Ġon ĠL DA Ġin Ġcold - start , Ġthe Ġcold Ġstart Ġproblem Ġoccurs Ġwhen Ġa Ġnew Ġitem Ġor Ġuser Ġhas Ġjust Ġlogged Ġinto Ġthat Ġsystem ; Ġit Ġis Ġdifficult Ġto Ġfind Ġsimilar Ġones Ġbecause Ġthere Ġis Ġnot Ġenough Ġinformation . ĠL DA Ġcan Ġbe Ġeffective Ġand Ġuseful Ġto Ġdeal Ġwith Ġcold - start Ġin Ġrecommendation Ġsystems . ĠThere Ġare Ġapproaches Ġbased Ġon ĠL DA Ġto Ġdeal Ġwith Ġthis Ġproblem Ġthat Ġsome Ġof Ġthe Ġmethods Ġare Ġcombined Ġwith ĠCF Ġmethods , Ġfor Ġexample , ĠLin Ġet Ġal , Ġinvestigated Ġthe Ġcold - start Ġissue Ġwith Ġuse Ġthe Ġsocial Ġinformation Ġfor Ġapp Ġrecommendation Ġin ĠTwitter Ġand Ġused Ġa ĠL DA Ġmodel Ġto Ġdiscovering Ġlatent Ġgroup Ġfrom Ġ' Twitter Ġpersonalities ' Ġto Ġrecommendations Ġdiscovery Ġand Ġshown Ġthat Ġtheir Ġapproach Ġover comes Ġthe Ġdifficulty Ġof Ġcold - start Ġapp Ġrecommendation . ĠAlso ĠSome Ġresearchers Ġinvestigated Ġthe Ġcold - start Ġproblem Ġin Ġtag Ġrecommendation , Ġfor Ġexample ; ĠIn ĠB IB REF 60 Ġ, Ġpresented Ġa Ġsystem Ġrecommendation Ġbased Ġon ĠL DA Ġfor ĠCold - Start Ġin ĠMusic ĠRecommend ation Ġand Ġshowed Ġthat Ġtheir Ġapproach Ġcan Ġbe Ġuseful Ġin Ġhandling Ġthe Ġcold Ġstart Ġproblem Ġwhere Ġa Ġnew Ġsong Ġhasn 't Ġoccurred Ġin Ġthe Ġtraining Ġdata . ĠOther Ġresearchers Ġalso Ġanalyze Ġthe Ġcold Ġproblem Ġin Ġthe ĠVideo Ġrecommendation , Ġfor Ġexample ; ĠIn ĠB IB REF 52 Ġintroduced Ġa Ġunified ĠYouTube Ġvideo Ġrecommendation Ġsolution Ġvia Ġcross - network Ġcollaboration Ġand ĠL DA Ġto Ġaddress Ġthe Ġtypical Ġcold - start Ġand Ġdata Ġsp arsity Ġproblems Ġin Ġrecomm ender Ġsystems Ġand Ġshow Ġthat Ġthis Ġapproach Ġcan Ġbe Ġeffective Ġin Ġterms Ġof Ġprecision Ġand Ġimproving Ġthe Ġdiversity Ġof Ġthe Ġrecommended Ġvideos . </s> Recommend ation ĠSystems Ġbased Ġon ĠL DA Ġfor Ġ' Sp arsity Ġproblem ', Ġthe Ġdata Ġsp arsity Ġchallenge Ġhappens Ġwhen Ġthe Ġratio Ġis Ġtoo Ġsmall Ġto Ġsupply Ġenough Ġinformation Ġfor Ġeffective Ġpredictions Ġin ĠCF Ġsystems , Ġand Ġhence Ġthe Ġaccess Ġmatrix Ġis Ġvery Ġsparse . ĠRecommend ation Ġsystems Ġbased Ġon ĠL DA Ġwith Ġratings Ġdata Ġcan Ġprovide Ġsignificant Ġadvantage Ġand Ġaddition Ġcan Ġbe Ġuseful Ġfor Ġexplor atory Ġdata Ġanalysis Ġand Ġdimension ality Ġreduction Ġin Ġhuge Ġcontent - text . ĠThis Ġdimension ality Ġreduction Ġcan Ġalso Ġhelp Ġto Ġalleviate Ġthe Ġsp ars eness Ġproblem Ġwhich Ġis Ġinherent Ġto Ġmany Ġtraditional Ġcollaborative - fil tering Ġsystems . ĠThere Ġare Ġapproaches Ġbased Ġon ĠL DA Ġto Ġdeal Ġwith Ġthis ĠSc arcity Ġproblem Ġsuch Ġas ĠB IB REF 76 </s> Recommend ation ĠSystems Ġbased Ġon ĠL DA Ġfor Ġ' user Ġlatent Ġinterests ', Ġlatent Ġinterest Ġrefers Ġto Ġlong - term Ġinterest Ġin Ġa Ġspecific Ġtopic , Ġin Ġfact ; ĠA Ġlatent Ġinterest Ġcan Ġbe Ġviewed Ġas Ġone Ġspecific Ġcharacteristic Ġof Ġthe Ġusers Ġand Ġthe Ġitems Ġwho Ġhave Ġthis Ġlatent Ġinterest Ġwill Ġprefer Ġthe Ġitems Ġwith Ġthis Ġcharacteristic . ĠFinding Ġa Ġcommunity Ġwith Ġa Ġlatent Ġinterest Ġin Ġanother Ġcould Ġhelp Ġin Ġrecommending Ġinteresting Ġnew Ġcommunities Ġfor Ġa Ġuser . ĠHowever , Ġfor ĠCF Ġsystems , Ġit Ġis Ġhard Ġto Ġidentify Ġthe Ġuser Ġlatent Ġinterests , Ġsince Ġthe Ġonly Ġinformation Ġavailable Ġis Ġthe Ġuser Ġinteraction Ġinformation Ġwith Ġthe Ġsystem . ĠWhile Ġtopic Ġmodels Ġcan Ġbe Ġutilized Ġto Ġsimulate Ġthe Ġuser Ġlatent Ġinterests Ġand Ġshowed Ġthe Ġway Ġof Ġextracting Ġthese Ġinterests Ġfrom Ġthe Ġlatent ĠDir ich let Ġallocation Ġ( L DA ) Ġmodel Ġby Ġthe ĠGibbs Ġsampling Ġmethod Ġin Ġour Ġexperiment . ĠWe Ġshould Ġnote Ġthat , Ġin Ġterms Ġof Ġitems , Ġa Ġlatent Ġinterest Ġcan Ġbe Ġviewed Ġas Ġone Ġspecific Ġcharacteristic Ġof Ġthe Ġitems Ġand Ġthe Ġusers Ġwho Ġhave Ġthis Ġlatent Ġinterest Ġwill Ġprefer Ġthe Ġitems Ġwith Ġthis Ġcharacteristic . ĠFortunately , Ġas Ġa Ġsimulation Ġtool , Ġthe Ġtopic Ġmodel Ġ( e . g ., ĠL DA ) Ġcan Ġbe Ġutilized Ġto Ġlearn Ġthe Ġmeaning , Ġsignificance , Ġcharacteristics Ġand Ġattributes Ġof Ġitems Ġin Ġa Ġdata - driven , Ġi . e ., Ġfrom Ġgiven Ġrating Ġrecords , Ġpossibly Ġwithout Ġfurther Ġcontent Ġor Ġprior Ġknowledge Ġof Ġthese Ġitems BI B REF 77 . </s> Conclusion </s> In Ġthis Ġpaper , Ġwe Ġpresented Ġa Ġtax onomy Ġof Ġrecommendation Ġsystems Ġand Ġapplications Ġbased Ġon ĠL DA Ġof Ġthe Ġrecent Ġresearch , Ġincluding Ġapp , Ġtravel , Ġfriend , Ġlocation , Ġscientific Ġpaper , Ġand Ġmusic Ġrecommendation . ĠFurthermore , Ġwe Ġapplied ĠL DA Ġalgorithm Ġand ĠGibbs Ġsampling Ġon ĠIS WC Ġand ĠWW W Ġconference 's Ġpublications Ġfrom Ġ2013 - 2017 . ĠGenerally , Ġrecommendation Ġsystems Ġcan Ġbe Ġan Ġimpressive Ġinterface Ġbetween Ġonline Ġusers Ġand Ġwebsites Ġin Ġthe ĠInternet Ġcommunities . ĠOur Ġstudy Ġsuggest Ġthat ĠN LP Ġmethods Ġbased Ġon ĠL DA Ġcan Ġdiscover Ġhidden Ġaspects Ġto Ġbetter Ġunderstanding Ġof Ġthe Ġbehaviors Ġof Ġthe Ġpeople Ġto Ġbuild Ġsmart Ġrecommendation Ġsystems Ġin Ġonline Ġcommunities .