the recent years , uwsns have emerged brilliantly for its appliances in management , control and surveillance in selected portions of deep oceans . the dynamic conditions , variations in topologies , energy constraints and high error probability during data forwarding are prominent challenges in the design of routing protocols in uwsn .the acquisition of real - time wireless data access in underwater environment is also a key demand for the future proposals in the field of uwsn routing protocols . unlike in terrestrial networks, the use of acoustic communication causes a larger propagation delay and low bandwidths , which has to be overcome in the proposed techniques . along with the scientific exploration in the deep sea water ,oil reserves management and coastline protection are the major demands from uwsn . in the recent literature ,distinguished techniques address the problems of high end - to - end delay , multipath fading and other mobility issues . in this paper , we strive to achieve global load balancing utilizing the adaptive movement of courier nodes and implementation of optimal weight computations for the sensor nodes .we also addresses the problems of high transmission and receiving power consumption in uwsn by employing better coordination among the nodes and minimal data forwarding . in uwsn, recent advances have shown the importance of long - term environment monitoring ; demonstrating longer lifetime of network as the key requirement in the future suggestions .the maintenance of higher throughput is also required during the entire network lifetime .dbr  and eedbr  propose the well - organized energy consumption schemes in the stability period but there is a lack of optimality in throughput during the instability period of network . in these techniques , the main load is on the low - depth nodes , causing their rapid energy consumption and coverage holes in network .there is a lack of load balancing in the typical routing protocols due to unequal load distribution among the nodes .self - management and self - optimization are the major achievements of our proposed technique in changing topology of uwsn . in aqueous environment ,the routing protocols are divided into two major categories : localization - free and localization - based routing schemes .localization - free routing protocols do not assume the localization information of sensor nodes .our scheme is also categorized in the latter category and its routing is based on the depth information of sensor nodes .routing protocols play a key role to prolong network lifetime . in this regard ,authors in  ,  ,  ,  and  proposed different schemes for terrestrial wsns . in recent researches, it has been proved that the delay - tolerant applications are the major intention of uwsn .therefore , the notable proposals in underwater routing protocols investigate lack of global load balancing in the network to obtain extended lifetime of network . an eminent technique in localization - free categoryis depth - based routing protocol ( dbr ) , based on data forwarding through low - depth sensor nodes .energy - efficient depth - based routing scheme ( eedbr ) is a constructive framework for maximizing the network lifetime by utilizing both depth and residual energy of the sensor nodes .it minimizes the end - to - end delay along with better energy consumption of the low - depth nodes .both of the afore - mentioned techniques strive to deal with the challenge of minimizing the load on medium - depth sensor nodes in dense conditions . tackle the challenges of uwsn by implementing the dynamic addressing scheme among the sensor nodes without requiring the localization information .another efficient scheme , r - erp2r  employs the routing metric based on the physical distances between the nodes and exercises it to accomplish higher throughput in uwsn .it also provides the energy efficient solution for data forwarding along with better link quality .the sensor nodes compute the holding time based on their depth during the optimal forwarder selection to eradicate the needless flooding of data packets .qelar  offers an outline for evenly distributed residual energy among the nodes to calculate the reward function .it strives to achieve longer network lifetime by selecting adequate forwarders for source nodes . in dense underwater conditions ,pulrp  provides layered architecture and detailed algorithm to achieve higher throughput among the network .it is free of localization information and do not require fixed routing table , minimizing the overhead in the network .in notable localization based protocols , hh - vbf  uses the vectors assumptions between the source and the destination nodes , effective for both dense and sparse conditions in the network .it gives a vector - based algorithm to achieve low end - to - end delay in the network in spite of no state information of sensor nodes .therefore , we propose adaptive mobility of courier nodes in threshold - optimized depth - based routing protocol ( amctd ) to attain reduced end - to - end delay , longer stability period and better network lifetime to cope with the varying conditions of uwsns .in the previous literature of routing protocols in uwsn , there is lot of prominent depth - based routing protocols .however , the efficient energy consumption remains the main acquisition due to necessity of longer network lifetime .dbr and eedbr struggle to obtain longer network lifetime but the stability period ends quickly due to unnecessary data forwarding and much load on low - depth nodes in uwsn . in the later technique , the nodes with high residual energy expire earlier due to increased load , causing less number of available neighbors for the remaining nodes in sparse condition and immense coverage holes in the core of network .the main deficiency of the depth based routing protocols such as dbr and eedbr is disorganized instability period due to the quick energy consumption of medium - depth nodes which has been addressed in our proposed scheme .however , eedbr removes key deficiencies of dbr but it has a lot of imperfections which have been overcome by our scheme as follows :  * the adaptive changes in depth threshold removes the problem of lack of availability of threshold based neighbors during the entire network lifetime . *the proficient movement of courier nodes minimizes the end - to - end delay as well as decrements the energy consumption of low - depth nodes utilizing on - spot data collection , making the scheme advantageous for data - sensitive purposes . *the optimal weight computation techniques cause longer stability period and also offer better throughput in the sparse conditions of network .  in this paper, we investigate the problems of little stability period , swift energy consumption of low - depth nodes and poor throughput during the instability period caused due to unequal load distribution among the nodes .we promote global load balancing in our proposed scheme by utilizing the optimized movement of courier nodes in sparse conditions .based on the all - above analysis , this paper presents adaptive mobility of courier nodes in threshold - optimized depth - based routing scheme ( amctd ) to accomplish resourceful energy expenditure of nodes in uwsn .in this section , we introduces our proposed protocol amctd descriptively . in the first section of network initialization ,each node computers its weight on the basis of density of network and the movement of courier nodes has been designed . in the second phase of data forwarding ,the optimal forwarders are decided on the basis of prioritization of weights of the neighbours of the source node . in the third section of weight updating and depth threshold adaption, network assigns the weights on the prioritization of depth and residual energy is changed according to the sparsity of network . in the last section , the movement of courier nodes and variations in depth threshold of nodes have been devised to cope with the sparsity of network .the afore mentioned steps have been discussed as following .   in this section ,we introduce our proposed protocol amctd descriptively . in the first section of network initialization, each node figures out its weight on the basis of density of network and the courier nodes initiate their schematic movement towards the surface of network . in the second phase of data forwarding, the source nodes decide the best possible forwarders on the basis of prioritization of weights among their threshold - based neighbors . in the third section of weight updating, network allocates the weights to sensor nodes , based on the prioritization of their depths instead of residual energy to cope with the sparse conditions of network . in the last section of depth threshold adaption , our routing scheme devises the precise course for motorized movement of courier nodes along with variations in depth threshold of nodes to deal with coverage holes created in the later rounds of network lifetime .the afore - mentioned steps have been discussed briefly as following .   in our first phase ,courier nodes devise their schematic sojourn tour in the network as the sensor node broadcast their depth information to the neighbors using hello packets .therefore , the joint communication between the sensor nodes , courier nodes and the sinks initializes the network operations .the key elements of network architecture design are as follows  * sink sends hello packet to all the nodes to get their vital information .* the network sets down the depth threshold of sensor nodes to 60 m to eliminate flooding process . *each node calculates its weights using the below - mentioned formula and employing the value of priority value .  where is the residual energy of node i , is the depth of node i and priority value is a constant .the weight calculation technique minimizes the load on the low - depth nodes , causing the increase in stability period .moreover , the computational formula for the weight of sensor nodes changes with the varying density of network .     during this phase , each sensor node shares its weight and depth information with its neighbors .employing hello packets transmission , each node identifies its neighbors in transmission range and maintains the separate queue of neighbors under depth threshold to identify the finest forwarder for its data transmission . as the network initializes, the courier nodes start their sojourn movements towards the surface of water . due to unlimited supply of energy , they aggregate the data of the sensor nodes continuously , transmit it to the sink and then go downward again to start the preceding sojourn tour . at one hand ,the courier nodes collect the data and on the other hand change its mobility pattern to adapt with the changing network density .their schematic mobility model helps out the network to diminish coverage holes creation in the instability period .each node also finds for the courier node among its neighbors to encourage on - spot data collection .. if courier nodes receive the packet of source node , it transmits acknowledgment to other neighbor nodes to stop further forwarding by any other neighbor of the source node .   after sensing data, node sends its data toward sink using the technique of csma / ca .source node finds the optimal forwarder among its threshold - based neighbors by comparing their weights .the neighbor having the highest weight is elected as forwarder and after receiving the packet it waits for holding time before upward data transmission .it discards the packet on receiving of same packet from any other neighbor node during the holding time duration .the node having lesser weight have much holding time , therefore , the optimal forwarder has smaller holding time duration then the other neighbors of source node .after every 50 rounds , the nodes broadcast hello packet in the network to find the number of dead node by the sink .it is used to cope with the changing conditions of the network and computations of network parameters .furthermore , if the two neighbors have same depth , the optimal forwarder will be one having more residual energy .if courier node receives the packet , it transmits acknowledgment to other neighbors of source node to eliminate needless forwarding by any other neighbor node .it utilizes the packet i d and source i d to accomplish this purpose causing resourceful energy consumption of the source node .the nodes continue to forward the packet of the source node until it reaches to the base station or courier node .this phase specifies the revisions in weight calculation of sensor nodes according to the altering node density of the network . after the number of dead node increases by 2 %, each node calculates its weight by the following formula this alteration is used to prioritize the depth among neighbors and to reduce the significance of residual energy in the calculation of the weight .it decreases the load on nodes with high residual energy and low depth to become optimal forwarder for consecutive transmissions .it chiefly plays role in incrementing the instability period of the network .   to adapt with the coverage problems of network, our design proposes the optimal variation in the depth threshold and the movement pattern of courier nodes , causing augmentation in the instability period .as the number of dead nodes increases by 75 % , first and third courier node starts to move between the depth of 355 m and the bottom of water , collecting the data from the high - depth sensor nodes .it is assumed that these nodes have minimal number of neighbors left alive mostly due to sparsity of the network .the speed of the movement of the courier node is also varied to a higher speed to facilitate the remaining alive nodes .moreover , the second and fourth courier nodes move vertically between 100 m and 200 m to accumulate the data from intermediate - depth nodes .the changing movement pattern mainly increases the network lifetime in the later rounds . in this section, we also discuss the well - organized framework for alterations in depth threshold . as the number of dead nodes increases by 2 % , the depth thresholdis decreased to 40 m to increase the quantity of threshold - based neighbors of the source nodes .it eases out the forwarding of data in low network density moreover , it is changed to 20 m as the dead nodes increase by 200 in extreme less dense condition to boost the network lifetime . in uwsns ,the network lifetime is of prime importance ; hence our scheme proposes the modification in weight calculation again as the number of dead nodes passes 80 % to prioritize the value of residual energy among the remaining alive neighbors of the source sensor nodes where is the residual energy of node i and is the depth of node i.in this section , we assess the performance of amctd in underwater sensor networks using matlab simulator . we compare the performance of our protocol with other energy efficient depth - based routing protocols such as dbr and eeedbr .both of the afore - mentioned protocols are considered to be an imperative landmarks in depth based routing schemes .   in order to analyze the results in practical scenario , we deploy the network of 225 nodes using random topology in 500mx500 m environment .the transmission range of sensor node is 100 meters , following the physical characteristics of underwater acoustic modem .we have adopted the specifications of commercial acoustic modem , linkquest uwm1000 , where the data generating rate is 1 packet per second .the power consumptions for the transmitting , receiving and idle mode are 2w , 0.1w and 10mw respectively .the initial energy of the node has been set to 70 joules while the packet size is 50 bytes . at the surface of water , the mutual distance between the sinks is 100 meters consecutively .the courier nodes reside in the bottom of water during the network initialization .then , they start to move upwards , transmit the received data to the sink and then again moves downward toward the bottom .each node broadcasts hello packets after 50 rounds to check the number of dead nodes from the sink . in every single simulation run , all the nodes of the network sense data andtransmit upwards , until it reaches to base station or courier nodes . to ensure efficient media access among the nodes , 802.11-dynav  protocol have been suggested .each node shares the vital physical metrics , especially depth threshold and weight with its neighbors to keep it informed with the varying circumstances of the network .our routing algorithm shapes the movement course of courier nodes to compensate with the sparse conditions of the network exploiting the transmission of hello packets . however , there is a trade - off between the overhead ( because of hello packets transmission ) and the availability of up - to - date information .the simulation parameters are given in table 1 .[ cols= " < , < , < " , ]   we demonstrate the following experimental metrics to express the performance of our proposed technique .  * network lifetime : it is the time duration between the network initialization and the complete energy exhaustion of all the nodes . * average energy consumption : it is the energy consumption of all the active nodes of network in 1 round . *probability of dropped packets : it shows the probability of loss of packets in 1 round . * number of dead nodes : it shows the number of dead nodes of the network . * confidence interval : it is an interval in which a measurement or trial falls corresponding to a given probability .we compare the network lifetime of our proposed technique amctd with dbr and eedbr . in the simulation of 15000 rounds, nodes have been deployed randomly in every simulated technique .figure 2 represents the comparison between the network lifetime of amctd , eedbr and dbr .the results of network lifetime comprises on the average of the 3 simulation runs of the afore - mentioned techniques and their comparisons , causing the increase in outcome authenticity . in every single round of simulation ,each alive node of the network send the packet , and the packet is forwarded until it reaches to the sink or courier node . in amctd ,first node dies at about 4500 round meanwhile , it also provides the instability period of about 12000 rounds . after the dying of initial nodes , the adaptive variations in the depth thresholdprovide longer lifetime to the network .the modifications in weight calculation technique and the prioritization of weight also encourages the efficient instability period .it causes the exclusion of abrupt energy consumption of sensor nodes due to better availability of threshold - based neighbors . in last 3000 rounds ,the movement pattern of courier nodes and their sojourn intervals enhance the probability of efficient data transmission . in the previous techniques of dbr and eedbr, the stability period ends quickly due to prioritization of residual energy or depth solely in the selection of optimal neighbors , which causes inefficient instability period . in our proposed technique ,resourceful utilization of energy becomes possible due to modification of depth threshold according to shifting network concentration .    in order to show the reliability of the estimates of simulation results ,figure 2 also shows the confidence intervals between network lifetimes of the contrasting techniques .confidence intervals consist of a series of values that operate as good estimates of the unknown results of network parameters .the first interval descriptively justifies the larger stability period of our proposed techniques which is better than the other depth - based routing protocols due to efficient energy consumption of the sensor nodes .the intervals throughout the network lifetime illustrate the consistency in the performance of our recommended technique avoiding the sudden collapse of network due to coverage holes creation in the network .figure 3 shows the evaluation of dead node variation in amctd , eedbr and dbr alongwith the average results of 3 simulation runs .the implementation of adaptive mobility of courier nodes improves the stability period of amctd then that of dbr and eedbr , which is also caused due to removal of redundant data forwarding by neighbor nodes .the key reasons behind the much capable instability period of our technique then of afore - mentioned schemes are changes in depth threshold and optimal forwarder assortment , prioritizing the sensor node depth in later rounds .the global load balancing is achieved by the low - depth movement of courier nodes , nevertheless in previous schemes low - depth nodes expire earlier due to a large extent of data forwarding . in dbr and eedbr ,high - depth nodes pass away rapidly in later rounds due to minimal quantity of neighbors .it affects the network harshly and the network throughput suddenly reduces because of swift raise in number of dead nodes .the optimized weight computation removes the same node maximal selection as a top forwarder ; hence curtailing the load the on nominal nodes and enhancing the network lifetime .the adaptive movement of courier nodes further diminishes the delay in data delivery to the base station .the confidence interval defines the accuracy of results along with removal of sudden collapse of network .figure 4 describes the comparison between the average energy consumption of network . in our scheme ,energy utilization of sensor nodes is much proficient at start due to effective weight implementation and slighter data forwarding .however , in the previous proposals , uneven energy consumption of nodes is cause due to elevated data forwarding towards the base stations by medium - depth nodes .it sources low stability period along with sharp reduction in the throughput of instability period .moreover , the higher energy utilization in our scheme in mid rounds is because of increase in the network throughput .it upholds the throughput and encourages smooth energy consumption by all the source and forwarding nodes .the proficient energy consumption causes enhancement in network lifetime and adjusts with the changing state of network concentration .the average energy consumption also undergoes a sudden drop off in the previous techniques due to the unequal distribution of load on specific low - depth nodes , causing low performance of network .a lot of empty spaces and coverage holes are created in the network during instability period which enlarge the probability for the loss of packets .the confidence intervals confirm that higher energy utilization in the previous techniques during the initial rounds origins the abrupt fall of network performance afterwards along with the poor coverage of network , therefore causing inefficient instability period .the confidence intervals of average energy consumption in our scheme encourage the equal energy utilization along the entire lifetime , which minimizes the coverage holes creation and encourages stable instability period .figure 5 estimates the throughput of network during the network lifetime . in our proposed technique, the presence of courier nodes and the changes in depth threshold largely enhances the reach ability of packets at base station . due to less decrement in residual energy of low - depth nodes , the network performance at the later roundsis maintained along with the constant end - to - end delay for packets .the validation improvement due to the computation of average results of 3 simulations not only justifies the advantages of our proposed scheme , but also discriminates its details from the previous depth - based routing protocols .figure 6 presents the comparison of probability of loss of packets in amctd , eedbr and dbr .we have used uniform random model to calculate the packet loss probability in our proposed method .the probability of loss of packets is increased in the previous techniques due to extensive distances between the source and destination nodes along with high depth thresholds . however , in our proposed framework , the loss probability decreases due to reduced distances involved in data forwarding and less electronic energy cost exploiting the variations in thresholds and mobile courier nodes .simulation results show that the probability of loss of packets in amctd is better than of eedbr and dbr of nodes over rounds .the adaptive movement algorithm of courier nodes and the execution of weight function reduce the number of lost packets along with the decrement in end - to - end delay of packets at sink node .figure 6 also illustrates the confidence intervals of probability of lost packets of the network .it not only shows the high loss probability in previous techniques due to longer forwarding distances , but also ensures the optimal link judgment in our proposed techniques which increase the network throughput even in the later rounds .in this paper , we recommend an adaptive mobility of courier nodes in threshold - optimized depth - based routing protocol to maximize the network lifetime of uwsn .our considerations are supportive in decrementing the energy consumption of low - depth sensor nodes specifically in the stability period .amendments in depth threshold for the sensor nodes enlarge the number of threshold - based neighbors in the later rounds , hence enhancing the instability period .optimal weight computation not only provides the global load balancing in the network , but also gives proficient holding - time calculation for the neighbors of source nodes .the adaptive movement of courier nodes upholds the network throughput in the sparse condition of network .as for future directions , we are striving to design much better courier nodes mobility pattern specifically toward the source nodes in the sparse conditions as well as dense conditions of network for network to perform equally fine in the complete lifetime of uwsns .we are also planning to integrate mac protocols  ,  and  with our routing scheme in order to facilitate the sensor nodes by the mobility of courier nodes , specifically in the sparse condition to achieve longer network lifetime .moreover , to increase the network lifetime we are interested to implement routing schemes like  ,  ,  and  in uwsns .  1 h. yan , z. j. shi , and j. h. cui , `` dbr : depth - based routing for underwater sensor networks '' , in networking 2008 ad hoc and sensor networks , wireless networks , next generation internet , pp . 7286 , springer , 2008 .m. akbar , n. javaid , a. a. khan , z. a. khan , u. qasim , `` on modeling geometric joint sink mobility with delay - tolerant cluster - less wireless sensor networks '' , 4th ieee technically co - sponsored international conference on smart communications in network technologies ( saconet13 ) , paris , france , 2013 .n. javaid , m. waseem , z. a. khan , u. qasim , k. latif and a. javaid , `` ach : away cluster heads scheme for energy efficient clustering protocols in wsns '' , 2nd ieee saudi international electronics , communications and photonics conference ( siecpc 13 ) , riyadh , saudi arabia , 2013 .b. manzoor , n. javaid , o. rehman , m. akbar , q. nadeem , a. iqbal , m. ishfaq , q - leach : a new routing protocol for wsns , international workshop on body area sensor networks ( basnet-2013 ) in conjunction with 4th international conference on ambient systems , networks and technologies ( ant 2013 ) , 2013 , halifax , nova scotia , canada , procedia computer science , volume 19 , 2013 , pp .926 - 931 .m. b. rasheed , n. javaid , z. a. khan , u. qasim and m. ishfaq , e - horm : an energy - efficient hole removing mechanism in wireless sensor networks , 26th ieee canadian conference on electrical and computer engineering ( ccece2013 ) , regina , saskatchewan , canada , 2013 .m. ayaz , a. abdullah , i. faye , and y. batira , `` an efcient dynamic addressing based routing protocol for underwater wireless sensor networks '' , computer communications , vol .4 , pp . 475486 , 2012 .t. hu and y. fei , `` qelar : a machine - learning - based adaptive routing protocol for energy - efcient and lifetime - extended underwater sensor networks '' , mobile computing , ieee transactions on , vol . 9 , no . 6 , pp . 796809 , 2010 .s. gopi , g. kannan , d. chander , u. desai , and s. merchant , `` pulrp : path unaware layered routing protocol for underwater sensor networks '' , in communications , 2008 .ieee international conference on , pp .31413145 , ieee , 2008 .x. peng , z. zhong , n. nicolas , c. jun - hong , and s. zhijie , `` efcient vector - based forwarding for underwater sensor networks '' , eurasip journal on wireless communications and networking , vol .2010 , 2010 .  d. shin and d. kim , `` a dynamic nav determination protocol in 802.11 based underwater networks '' , in wireless communication systems .ieee international symposium on , pp .401405 , ieee , 2008 .a. rahim , n. javaid , m. aslam , u. qasim , z. a. khan , `` adaptive - reliable medium access control protocol for wireless body area networks '' , poster session of 9th annual ieee communications society conference on sensor , mesh and ad hoc communications and networks ( secon2012 ) , seoul , korea , 2012 .a. n. alvi , s. s. naqvi , s. h. bouk , n. javaid , u. qasim , z .a. khan , `` evaluation of slotted csma / ca of ieee 802.15.4 '' , broadband , wireless computing , communication and applications ( bwcca ) , 2012 seventh international conference on , vol ., no . , pp.391,396 , 12 - 14 nov . 2012 .a. rahim , n. javaid , m. aslam , a. rahman , u. qasim , z. a. khan , `` a comprehensive survey of mac protocols for wireless body area networks '' , broadband , wireless computing , communication and applications ( bwcca ) , 2012 seventh international conference on , vol ., no . , pp.434,439 , 12 - 14 nov . 2012 .k. latif , a. ahmad , n. javaid , z. a. khan and n. alrajeh , `` divide - and - rule scheme for energy efficient routing in wireless sensor networks '' , the 4th international conference on ambient systems , networks and technologies ( ant 2013 ) , 2013 , halifax , nova scotia , canada , procedia computer science , volume 19 , 2013 , pp. 340 - 347 .n. javaid , s. n. mohammad , k. latif , u. qasim and z. a. khan , m. a. khan , `` heer : hybrid energy efficient reactive protocol for wireless sensor networks '' , 2nd ieee saudi international electronics , communications and photonics conference ( siecpc 13 ) , 2013 , riyadh , saudi arabia .m. aslam , t. shah , n. javaid , a. rahim , z. rahman , z. a. khan , `` ceec : centralized energy efficient clustering a new routing protocol for wsns '' , poster session of 9th annual ieee communications society conference on sensor , mesh and ad hoc communications and networks ( secon2012 ) , seoul , korea , 2012 .n. javaid , s. n. mohammad , k. latif , u. qasim and z. a. khan , m. a. khan , `` heer : hybrid energy efficient reactive protocol for wireless sensor networks '' , 2nd ieee saudi international electronics , communications and photonics conference ( siecpc 13 ) , riyadh , saudi arabia , 2013 .

Abstract: in dense underwater sensor networks ( uwsn ) , the major confronts are high error probability , incessant variation in topology of sensor nodes , and much energy consumption for data transmission . however , there are some remarkable applications of uwsn such as management of seabed and oil reservoirs , exploration of deep sea situation and prevention of aqueous disasters . in order to accomplish these applications , ignorance of the limitations of acoustic communications such as high delay and low bandwidth is not feasible . in this paper , we propose adaptive mobility of courier nodes in threshold - optimized depth - based routing ( amctd ) , exploring the proficient amendments in depth threshold and implementing the optimal weight function to achieve longer network lifetime . we segregate our scheme in 3 major phases of weight updating , depth threshold variation and adaptive mobility of courier nodes . during data forwarding , we provide the framework for alterations in threshold to cope with the sparse condition of network . we ultimately perform detailed simulations to scrutinize the performance of our proposed scheme and its comparison with other two notable routing protocols in term of network lifetime and other essential parameters . the simulations results verify that our scheme performs better than the other techniques and near to optimal in the field of uwsn .   weight function , courier nodes , underwater wireless sensor networks , depth - based routing .


with the affordable prices of gps - enabled mobile devices and the success of social networks , location - based social media has become increasingly popular in recent years .users can upload content , e.g. , photos , videos , and text , and annotate that content with geographical identification metadata , typically known as _geotags_. geotags act as geographic indexes helping users to organize and retrieve location - specific information .foursquare , as an example , is a location - based service where users endorse and share tips about visited points of interest ( poi ) .it reached 725 thousand registered users and 22 million check - ins ( i.e. endorsed pois ) in 2010 .recommender systems ( rs ) are among the best known techniques for helping users filter out and discover relevant information in large data sets . in the typical scenario , rs algorithms exploit user - item matrices representing user preferences for items , e.g. , the rating history of purchased books in amazon , with the aim of recommending the items most likely to be relevant to the user . while most of the rs work to date has ignored the locations where users demonstrated interest for an item , there are many scenarios in which the geographic context of an item has a direct influence on the preferences of the user for that item .mao et al . , for example , showed that foursquare users prefer pois that are nearby the pois they already visited in the past , while showed that panoramio users who took pictures in nearby locations in the past , tend to share similar preferences of geographic regions in the future .efficient extraction and representation of location - specific user preferences are thus essential to decide on what item to recommend .location - aware recommender systems suggest relevant geotagged items for a given user within a declared geographic area .relevance here can assume different notions , depending on the geographic constraints imposed by the user .for example , a user may be interested in objects nearby his previous , current , or future location within a given radius .for example , a first time user visiting the stanford university campus in palo alto , us , might be interested to know what is worth visiting inside the campus , while a second time visitor may want to know what else is worth visiting . each of these scenarios can lead to various definitions of user preference , hence it is important to know which definition works best for each scenario .the literature concerning location - aware recommender system is still sparse , where the methods are ad - hoc and can not be easily changed to meet the different recommendation scenarios outlined above ( e.g.  ) .  in this paperwe propose a relational graph for capturing the geographic context of users that suits all the aforementioned recommendation scenarios .we introduce several strategies to represent location - specific user preferences in the graph and show how to derive many recommendation algorithms , including ensembles of them , by only changing the definition of the edge weights .our contributions are as follows :1 .  we propose a new model for geotagged data that is able to capture both the geographic context of users and their preferences for objects within the declared geographic context .we introduce new similarity measures that take into account the spatial decisions of users .we propose a recommendation algorithm based on a relational neighbor graph , that derive many recommendation algorithms and ensembles of them , as special cases , by only requiring changes in the definition of the edge weights .4 .  finally , we conduct experiments using two datasets in various recommendation scenarios , including cold - start ones , and provide empirical evidence that many of the proposed algorithms outperform existing location - aware recommender algorithms .the rest of the paper is organized as follows .section [ sec : problem ] introduces the problem setting .section [ sec : approach ] presents our relational graph representation of the data , weighting schemes for capturing user geographic preferences , and a recommendation algorithm based on relational neighbors. section [ sec : experiments ] presents the experimental setting and evaluation .section [ sec : related_work ] describes related work , and section [ sec : conclusions ] concludes the paper and discusses future work .the recommendation scenario is as follows .a user specifies the geographic region of interest , e.g. , a city or a region in that city , and the recommender engine suggests items within the declared geographic region that are likely to be relevant to the user .so , let be the set of users , the set of regions denoting geographic contexts , and the set of geotagged items . notice that what is meant by a region is application dependent since regions can assume different geographic shapes , such as point coordinates , circles , lines , and polygons . in this paper we only consider implicit feedback data , i.e. , the set of ternary relations between users , geographic contexts , and geotagged items .the task is then to find a prediction scoring function that predicts a preference score for items within certain geographic regions , given a target user .now , for a given user , and a given geographic context , the topn recommendations can be computed by where denotes the topn items to be recommended and the set of items within the geographic context . for convenience ,we also define as the set of items of user in a given geographic context .relational classification refers to an active area of machine learning where classifiers usually consider , additionally to the typical attribute - value data of objects , relational information .a scientific paper , for example , can be connected to another paper that has been written by the same author or because they share common citations .it has been shown that in many classification problems , relational classifiers outperform purely attribute - based classifiers  .in particular , macskassy and provost  showed that simple relational neighbor - based techniques , besides requiring low computational costs , perform competitively to , and in some cases even outperforms , more complex relational methods such as probabilistic relational models and relational probability trees .the basic idea is that the classification of a target instance solely depends on the class labels of related instances of the same type .since geotagging data is inherently relational , we propose to capture the geographic context of users and items by a relational graph , which , as a side effect , gives us many tools from relational classification that can be directly applied to the location - aware recommendation problem .  in order to easily use neighborhood - based classification methods , similarly to , we adopt a homogenous view of the relations in the data . in a homogeneous viewwe have only one entity type , such that , there is a set of target entities and relations between them .however , as we saw in section [ sec : problem ] , geotagging data forms a set of ternary relations between three different types of entities .therefore , we first need to convert these ternary relations into the desired homogeneous relations .we do this as follows .first let denote the set of all distinct user / geographic context combinations in .we now propose to interpret as a graph , where is the set of vertices and the set of edges .we assume that there is an edge between two vertices if they share the same geographic context , i.e. , for convenience , let and denote the geographic context and the user of node respectively . in other words , we assume that users who share the same geographic region are related to some extent .now suppose that john is a first time visitor in rio de janeiro , brazil , and wants to know from other people who already have been in rio which places are worth visiting there .to continue our example , we denote the pair john / rio by a colored node in the graph of figure [ fig : graph ] , i.e. , the target node for which we want to compute recommendations .john is denoted as and rio as respectively , and the other nodes connected to it contain the users who already have been in rio . for computing recommendations ,we just need to go through the items of the neighbors and define some selection criterion on which items to recommend .this idea assumes that entities related to each other , in this case users sharing the same geographic context , are similar and tend to select the same items .notice that the strength of the similarity depends on the size of the geographic region being considered .if the declared geographic context is a small region , say the copacabana beach in rio de janeiro , then it is more likely that users within this area will be more strongly related to each other than users sharing larger geographic contexts , such as the whole country of brazil .we can alleviate these effects by defining appropriate weights to each relation , as we will see next .     in this section we present several weighting strategies for the edges of the relational graph .and as we will see in the next section , each weighting scheme leads to a specific recommendation algorithm , without , however , the need to change the overall algorithm .[ [ uniform - weighting ] ] uniform weighting + + + + + + + + + + + + + + + + +  here the same weight value is assigned to each edge ( in our case ) , denoting that each neighbor is of the same importance to the target user .therefore , the weight for any edge , is defined as .the computational cost for assigning uniform weights to the relations of a target node is in the order of , i.e. , a linear scan in the neighbors of , here denoted by .[ [ correlation - weighting ] ] correlation weighting + + + + + + + + + + + + + + + + + + + + +  for two nodes , we can represent and as profile vectors , where each component of the vector is a geotagged item and the values denote the preference of a user for an item , i.e. , we can either construct this vector solely based on the items of the geographic context of interest , or we could consider the items of all geographic contexts .the assumption is that users who have selected the same items within the same geographic contexts are more similar than users who did not .if a user declared interest for some geographic context for which he has not yet selected any item , it will not be possible to compute any correlation similarity with other users , unless we build the profile vectors considering the items of other geographic contexts .the edge weight between two nodes is finally computed by applying a correlation metric between the desired nodes profile vectors , which in our case , is the cosine similarity :   the computational cost for assigning cosine similarities to the relations of a target node is in the order of since we need to compute similarities , each requiring operations .[ [ geographic - similarity ] ] geographic similarity + + + + + + + + + + + + + + + + + + + + +  we can use the geographic distances between users items to define the strength of their relation .the assumption is that users who select items nearby the items of other users should be closely related .therefore , for defining the weight between two nodes through geographic similarity , we first calculate the geographic centroids of the set of items of users and . for computing the geographic centroid of a given user , we sum up the coordinates of each geotagged item of user within the declared geographic context , i.e. , the items in the set , and divide the resulting sum by the number of items in the set where returns the latitude / longitude coordinate used to geotag item .now , the geographic distance between and centroids is given by any of the many existing functions for calculating geodetic distances between latitude / longitude coordinates , e.g. the haversine formula .here we denote such a function by where and are two coordinates . finally , we only need to turn the distance into a similarity and bound it to the range  ] + $ ] +  [ algo : location ]in this section we describe our two datasets , geotagged photos from panoramio , and print jobs from the hp eprint mobile print location service ; the evaluation protocol adopted ; and the results for each dataset .we considered three different recommendation scenarios .  in the first scenario ,we hide all the geotagged items of each test user in a geographic context , and use the remaining data for trying to predict the removed items .this corresponds to a cold - start scenario where a user has not selected any item in the geographic context of interest .we will refer to this scenario as _ leave - all - out_.  in the second scenario , we remove some geotagged items for each test user , 4 photos in panoramio and 1 printer provider in eprint , and use the remaining data for predicting the removed items .this scenario represents those users who already selected some items in a given location but want to know what other items are worth selecting in this location .we will refer to this scenario as _ leave - some - out _  in the third scenario , we have a mix of both scenarios , i.e. , some fraction of the users are first time users and the other fraction already have selected some items in the location of interest .this corresponds to a more realistic scenario , and to the best of our knowledge , this is the first time location - aware algorithms are evaluated in this kind of scenario .we will refer to this scenario as _ leave - some / all - out _   panoramio is a photo - sharing website from google where users can upload , geotag , and retrieve photos of landmarks .each photo in panoramio is georeferenced using latitude and longitude information .similarly to  , we assume that if a user takes a picture in a specific location , then he has some interest in that location . as geographic context of interestwe have chosen the city of rio de janeiro , which is one of the top touristic places in brazil , so , it has a large set of photos .[ [ data - collection - and - preparation ] ] data collection and preparation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +  in order to retrieve the set of photos taken in rio , we did a spatial search for photos inside the bounding box of rio using the data access api of panoramio .we then iterated through each photo in the result set , and retrieved the users who took these photos . for each of these users, we then retrieved the other locations where they took photos , as well as the users and photos in those locations .then we removed the cities with too little activity from users who also visited rio .approximately the top three cities in all the crawled states were kept in the evaluation set .we used the gazetteer of hp gloe to obtain the place names .  in order to use the _ two - layer - similarity _ weighting scheme presented in section [ sec : weight ], we built a geographic partonomy , and similarly to  , we worked with three countries ( brazil , chile , and usa ) as high level nodes , states and cities as intermediary nodes , and geographic clusters as leaf nodes . our data set contains 35,920 photos ( 4,906 from rio ) and 7,048 users in total .in panoramio the geographic items are represented by latitude and longitude coordinates , hence , it is very difficult for two users to take a picture in the exact same location .therefore , we adopted the same approach as in  , where the authors used geographic clusters to represent geographic items .so , instead of recommending individual point coordinates , we recommend regions where users may be interested in taking photos . for computing the clusters , we used the dbscan  algorithm with the following parameters : km and .the parameter was set to 1 km , as we assume that a radius of 1 km from a given photo is sufficient to establish a geographic similarity between photos , e.g. , photos taken at the university of stanford campus .we tested to set meters without any significant differences in performance , so we do not show those results here . also , the minimum number of points to form a cluster was set to 3 , in order to establish popularity of a given poi .we computed 1,187 clusters , 221 of which are in rio .[ [ evaluation - protocol ] ] evaluation protocol + + + + + + + + + + + + + + + + + + +  for testing the algorithms , we considered the dense part of the data , i.e. , only the users who have taken at least 5 photos in rio ( see table [ tab : dataset ] ) .for the _ leave - some - out _ and _ leave - some / all - out _ scenarios , we generated 5 random splits of training / test sets and took the average precision and recall on top- recommendation lists over all splits .we have a hit every time a hidden photo is found to belong to some of the recommended geographic clusters .whenever cf is not able to fill the recommendation list up to 10 , we fill up the list with the most popular items that are not already in the recommendation list .  .panoramiodata for rio de janeiro [ cols=">,>,>",options="header " , ]   [ [ algorithms ] ] algorithms + + + + + + + + + +  we have used several weighting schemes for our relational graph , which resulted in the following recommendation algorithms :  * * most popular ( mp ) : * recommends the most popular geographic clusters in rio . for doing that, we just apply uniform weighting ( see section [ sec : weight ] ) to the graph in algorithm [ algo : location ] . ** intra - cluster ( ic ) : * uses the geographic similarity defined in section [ sec : weight ] for defining intra - cluster similarities between users .the idea is as follows .the weight of any edge is now calculated by summing up for all clusters where and have photos , and dividing the resulting sum by the total number of clusters .after that , we again just need to plug the weighted graph into algorithm [ algo : location ] for computing recommendations . * * collaborative filtering ( cf ) : * this is the geographic version of the classic user - based collaborative filtering algorithm .we use the correlation weighting scheme defined in section [ sec : weight ] where profile vectors components are the geographic clusters where users took pictures , within and out of the context of interest . ** two - layer ( tl ) : * this is the algorithm originally introduced in  .we weigh the relational graph with the _ two - layer similarity _ and plug it into the recommendation algorithm . * * correlation + two - layer ( cf - tl ) * : we found empirically that tl works best for cold - start scenarios , while cf outperforms the other algorithms for non cold - start scenarios ( see figures [ fig : some_out ] and [ fig : all_out ] ) .this gave us the insight to propose a weighting combination strategy where we weigh each relation differently according to the case presented .if the target user is a cold - start user , we weigh his relations with the _ two - layer similarity _ , if not , we use the _ correlation weighting_. the flexibility for combining different weighting schemes in such an easy way is one of the main advantages of our approach .[ [ results ] ] results + + + + + + +  figure [ fig : some_out ] depicts the results for the _ leave - some - out _ scenario .notice that when there is enough data available , cf outperforms all the other methods .the intra - cluster recommender , although worse than cf and tl , is better than mp in all cases .this indicates that the geographic similarity indeed is able to capture some preferences of the user , under the assumption that users tend to like the items of other users that are nearby the items they have selected in the past .figure [ fig : all_out ] shows the results for the _ leave - all - out_. notice that tl is the winner in this scenario .this is in line with the results of  , where they showed that geographic partonomies can help to improve the recommendations in cold - start scenarios .notice that since we remove all the photos of all test users in the context of interest , we just have one possible split of training / test , and thus can not compute standard deviations and plot error bars .      in figure[ fig : mix_05 ] we show the results of the _ leave - some / all - out _ scenario when 50% of the test users are cold - start users . in this case , the combination method cf - tl is slightly better , both in precision and recall , than the other methods .but when 70% of ther users are cold - start users ( see figure [ fig : mix_07 ] ) the superiority of cf - tl becomes more evident .we also evaluated this scenario when 30% of the users are cold - start users , but since there was no significant differences in performance in comparison to 50% of cold - start users , we do not show those results here .the hp eprint mobile print location ( mpl )  service allows smartphone users to print documents , photos , web pages and emails directly to public print providers , such as business centers at hotels and airports , and dedicated copy and print service stores .users can query for providers near their current location and then submit a print job upon which a pickup code is generated .this code can then be used in the store to obtain the printout .  [ [ data - preparation ] ] data preparation + + + + + + + + + + + + + + + +  our dataset contains a 5-month trace of all print jobs sent through the service . to protect business sensitive informationthe users as well as providers were anonymized .furthermore , the volume was obfuscated using a bootstrap inspired sampling algorithm that takes uniform random samples with replacement of print jobs from the original dataset and replays them with the original timestamp randomly modified up to one week from the original job submission .we sampled 100k print jobs in this way , and then extracted all the entries that occurred in manhattan ( about 2,580 ) .each entry comprises the user i d of the user submitting the print job , the provider i d of the provider receiving the print job , the timestamp of the print job and the latitude and longitude of the print provider location .[ [ evaluation - protocol-1 ] ] evaluation protocol + + + + + + + + + + + + + + + + + + +  here the geographic items are print providers and in contrast to panoramio , the pois are well defined , and therefore we do not need to compute geographic clusters .we chose manhattan as the geographic context of interest because of its large volume of printing activity .here we used the _ leave - one - out _ protocol , i.e. , for each user we randomly removed one of the items and used the rest for computing recommendations .we considered all the users who have used at least one print provider , i.e. , all users in manhattan , for our test set . due to the nature of the data set, we only evaluate the _ leave - some / all - out _ scenario , where some users are cold - start users and others not .furthermore , we ignore users for which the recommendation list is empty .since we only hide one photo , we do not compute precision , because this would be the same as recall up to a multiplicative constant .thus , we only compute the average recall over ten random splits of training / test . again , due to the nature of this dataset we only evaluated the mp and cf algorithms for top- recommendations . whenever cf is not able to fill the recommendation list up to 10 , we fill up the list with the most popular items that are not already in the recommended list .[ [ results-1 ] ] results + + + + + + +  figure [ fig : eprint_results ] shows the results for mp and cf in eprint .the error bars represent one standard deviation .we can see that the most popular item algorithm outperforms the collaborative filtering algorithm consistently , in particular for long recommendation lists . given the relative short period this service has been on the market it is dangerous to draw too many conclusions from this behavior , but there are two lessons to be learned from this result .first , we could easily adapt our algorithm to generate useful recommendations for a use case vastly different than the panoramio application , by just simply applying a different weighting scheme .second , a very simple popularity - based recommendation engine can do very well , and deploying sophisticated collaborative filtering techniques may not always be the best choice .related work falls into three broad categories : relational classification and similarity measures , location - aware methods , and methods focusing on improving the computational scalability of the model building and execution phases .[ [ relational - classification - and - similarity - measures ] ] relational classification and similarity measures + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +  relational classification has been applied to areas where entities are linked in an explicit manner , like hypertext documents , such that the class of a target instance only depends on the class of its related instances  . in  we presented a relational neighbor classification approach in the ecml / pkdd discovery challenge 2009 , which was about tag recommendations . similarly to that work , in this paper we formulate the recommendation problem in a relational neighbor framework where geographic contexts link entities represented by user / location pairs .  in  a new similarity measure for computing location recommendations based on a non - overlapping hierarchical taxonomy of locationsis presented .the key idea is that co - activity in locations can be better captured if you zoom out to larger and larger locations .similar to our work they use a panoramio data set to evaluate their algorithm .however , their model assumes a cold - start user is making the query , i.e. a user who has no trace in the geographic context of the query .the reliance on a place taxonomy is also more restricting than our general model .jeh and widom define simrank in  .the simrank similarity measure is as the name suggests highly influenced by the more famous pagerank  algorithm .the general idea is that `` two objects are similar if they are referenced by similar objects ''  .this definition recursively propagates similarity through a relational graph to leverage structural context in addition to the more traditional use of object content and attribute information . in our workakin to the model used in  , we also have a recursive , relation - based definition of similarity but in the form of hierarchical geographic contexts . for faster computationwe simplify the similarity model to not be recursive between leaf - nodes within the same geographic context in our graph .our evaluations show that this can be done without any loss of accuracy if the user who requested the recommendation already has an activity trace within the geographic context for which the recommendation is sought .minkov and cohen  also explore relational properties among objects to compute similarity scores .they focus on the problem of effectively searching in graphs comprised of interrelated objects of various types .they propose effective random - walk based procedures to evaluate the personal pagerank measure  , a measure that takes into account the scope of the query in addition to the well - known random surfer assumption of the original pagerank algorithm .[ [ location - aware - methods ] ] location - aware methods + + + + + + + + + + + + + + + + + + + + + +  in  a standard user - based collaborative filtering approach is extended with a novel mechanism of geographic influence based on a statistical model of how likely a user is to check - in to two places based on their distance .the geographic model is a distributional assumption ( power law ) fitted to real data sets from two popular check - in services .a number of studies have looked at timestamped gps traces to predict future locations within very restricted geographic regions  .markov models and tensor factorization models are fit to the data and non - personalized predictions of the most likely next location or the most likely activity given a location and time are produced .there is a lot of novel work on automatically detecting geographic context in these papers but the general approach can not be replicated easily in our scenario since we do not have the same luxury of rich traces as we mainly focus on implicit feedback as input . relying on gps traces is furthermore a privacy concern and has scalability and power consumption implications .we produce more personalized recommendations given user - user similarities as opposed to just looking at the most popular or most frequent behavior .furthermore large markov models and tensor factorization algorithms tend to be very costly to compute , which would therefore need to be done in an off - line setting whereas we also target real - time recommendations .bayesian networks have also been studied to model and learn patterns in location , time and weather contexts for individual users in  .however , compared to our model these models tend to be complex and require expert human knowledge to construct , and furthermore they are not tractable . applies a center of mass model to detect and recommend locations and pois .this work was before the check - in systems era so now we could just more easily query the check - in services for this information . in  user - user based cf with location pre - filteringis employed in an explicit voting scenario .the cold start problem is solved by generating random recommendations using pseudo users .we address the problem by incorporating out - of geo context similarities for in - context recommendations which is less ad - hoc .  in our previous work in  we studied popularity inferred recommendations based on location , friends and tag prefiltering using both explicit and implicit feedback .in this work we extend that model to provide personalized recommendations similar to the work in  but with a new model that incorporates location , and distance metrics directly in the evaluation graph as opposed to relying on ad - hoc and costly pre and post filtering .the geofolk system  was designed to take both geographic context and text features into account for various information retrieval tasks such as tag recommendation , content classification and clustering .experiments show that combining both textual and geographic relevance leads to more accurate results than using the two factors in isolation .although our methods and use case targets are quite different from this work , the empirical evidence of the influence geographic context has on information retrieval is promising and serves as motivation for our work .  [ [ computational - scalability ] ] computational scalability + + + + + + + + + + + + + + + + + + + + + + + + +  popular and accurate recommender system methods such as those based on matrix factorization can incure very high model building as well as execution overhead , in particular as more contextual factors beyond users and items are taken into account . as a resultthere have been many attempts at improving the computational scalability of pre - existing methods .  in  the general issue of complex and high - latency model building and execution for location - based recommendations is addressed . to achieve real - time performance  and  pre - filter based on location and  pre - filters based on friends to reduce the complexity of the models .as opposed to pre- or post - filtering context we make contextual paramaters an inherent part of our graph model to allow interesting combinations of various types of context in an efficient way .  in a fast context - aware recommendation algorithm is proposed that maintains the features of state - of - the - art multi - tensor matrix factorization while bringing down the complexity of the previously known algorithms from exponential to linear growth in problem size .the main idea is to solve the least - squares optimization problem for each model parameter separately .our method in contrast achieves scalability by not utilizing any complex matrix factorization .in this paper we introduced a relational graph for capturing the geographic preferences of users with the purpose of generating personalized recommendations in services with geotagged content .we also presented several weighting schemes for representing different types of user preferences in the proposed graph .furthermore , we propose a recommendation algorithm template that is sufficiently generic to derive many traditional and new location - aware recommendation algorithms , including combinations of them , by only requiring changes in the definition of the edge weights . assuming the graph is given , the algorithm requires modest computational effort since it runs linearly in the number of neighbors .we have tested the proposed algorithms with two real - world datasets , geotagged photos from panoramio and print jobs from the hp eprint mobile print location service , and showed how our model easily suits many different recommendation scenarios .we also gained insights about which notion of similarity works best for a set of scenarios .in cold - start scenarios a geographic partonomy seems to be a good alternative , whereas when there is enough data available the plain location - aware collaborative filtering algorithm yields the best result . in response to this finding , we proposed to combine a partonomy - based similarity measure with the cosine similarity by weighting individual relations in the graph according to the type of the user , i.e. , cold - start versus non cold - start . by doing this , we achieved better recall and precision in particular in scenarios where there are many cold - start users .as future work , we plan to incorporate temporal aspects in the model , such that the items to be recommended match the temporal context of the user .for example , it may not make as much sense to recommend ski resorts in new york during the summer as it would to make the same recommendation during the peak winter season .another natural extension of our work would be to assign the weights in our graph based on the strength of the social ties between the users , e.g. based on their declared or implied social networks .finally , we plan to investigate machine learning approaches for learning optimal weights based on the location - aware recommendation task at hand .this work was supported by a cooperation with hewlett - packard brasil ltda . using incentives of brazilian informatics law ( law no .8.2.48 of 1991 ) .we would also like to thank christina aperjis , sitaram asur and mao ye for insightful comments on our work .s. brin and l. page .the anatomy of a large - scale hypertextual web search engine . in _ proceedings of the seventh international conference on world wide web 7 _ , www7 ,pages 107117 , amsterdam , the netherlands , the netherlands , 1998 .elsevier science publishers b. v.  m. brunato and r. battiti .pilgrim : a location broker and mobility - aware recommendation system . in _percom 03 : proc .of the first ieee international conference on pervasive computing and communications _ , pages 265. ieee computer society , 2003 .i. burbey and t. l. martin .predicting future locations using prediction - by - partial - match . in _melt 08 : proc .of the first acm international workshop on mobile entity localization and tracking in gps - less environments _ , pages 16 .acm , 2008 .t. horozov , n. narasimhan , and v. vasudevan .using location for personalized poi recommendations in mobile environments . in _ proc .of the international symposium on applications on internet _ , pages 124129 .ieee computer society , 2006 .g. jeh and j. widom .simrank : a measure of structural - context similarity . in _ proceedings of the eighth acmsigkdd international conference on knowledge discovery and data mining _ , kdd 02 , pages 538543 , new york , ny , usa , 2002 .q. lu and l. getoor .link - based classification using labeled and unlabeled data . in _ proceedings of the icml workshop on the continuum from labeled to unlabeled data in machine learning and data mining _ , 2003 .c. matyas and c. schlieder . a spatial user similarity measure for geographic recommender systems . in k. janowicz , m. raubal , and s. levashkin , editors , _ geospatial semantics _ , volume 5892 of _ lecture notes in computer science _ ,pages 122139 .springer berlin / heidelberg , 2009 .s. rendle , z. gantner , c. freudenthaler , and l. schmidt - thieme .fast context - aware recommendations with factorization machines . in _ proceedings of the 34th international acm sigir conference on research and development in information _ , sigir 11 , pages 635644 , new york , ny , usa , 2011 .t. sandholm and h. ung .real - time location - aware collaborative filtering of web content . in _carr 11 : proc . of the 2011 workshop on context - awareness in retrieval and recommendation_ , pages 1418 .acm , 2011 .s. sizov .geofolk : latent spatial semantics in web 2.0 social media . in _ proceedings of the third acm international conference on web search and data mining _ , wsdm 10 , pages 281290 , new york , ny , usa , 2010 .m. ye , p.yi , w .- c .lee , and d .-exploiting geographical influence for collaborative point - of - interests recommendation . in _sigir 11 : proc . of the 34th acm sigir conference_. acm , 2011 .( to appear ) .

Abstract: with the increasing popularity of location - based social media applications and devices that automatically tag generated content with locations , large repositories of collaborative geo - referenced data are appearing on - line . efficiently extracting user preferences from these data to determine what information to recommend is challenging because of the sheer volume of data as well as the frequency of updates . traditional recommender systems focus on the interplay between users and items , but ignore contextual parameters such as location . in this paper we take a geospatial approach to determine locational preferences and similarities between users . we propose to capture the geographic context of user preferences for items using a relational graph , through which we are able to derive many new and state - of - the - art recommendation algorithms , including combinations of them , requiring changes only in the definition of the edge weights . furthermore , we discuss several solutions for cold - start scenarios . finally , we conduct experiments using two real - world datasets and provide empirical evidence that many of the proposed algorithms outperform existing location - aware recommender algorithms .


in modern communication systems , interference is one of the fundamental factors that limit performance : a receiver is only interested in retrieving information from its own transmitter , while the information - carrying signals become interference to other users due to the broadcast and superposition nature of wireless channels .the simplest information theoretic model for studying this issue is the _ interference channel_. characterizing the capacity region is a long - standing open problem , except for several special cases .recently etkin , tse , and wang characterize the capacity region of the gaussian interference channel to within one bit by using a superposition coding scheme with a simple power - split configuration and by providing new upper bounds .  in the above interference channelset - up , transmitters or receivers are not allowed to communicate with one another , and hence each user has to combat interference on its own . in various applications , however , nodes are not isolated , and transmitters / receivers can exchange certain amount of information . since the nodes are distributed due to the physical constraints , the amount of information they can exchange is limited . therefore , one of the fundamental questions is , how much _ interference _ can limited _transmitter / receiver cooperation _ mitigate ?  in this paper , we consider a two - user gaussian interference channel with _ conferencing decoders _ to answer this question regarding receiver cooperation .conferencing among encoders / decoders has been studied in , , , , , and .our model is similar to those in and but in an interference channel set - up .the work in characterizes the capacity region of the compund mac with unidirectional conferencing between decoders and provides achievable rates for general set - up but is not able to establish a constant - gap result .the work in considers one - sided gaussian interference channels with unidirectional conferencing between decoders and characterizes the capacity region in strong interference regimes and the asymptotic sum capacity at high . for general receiver cooperation , works including and , investigate cooperation in interference channels with a set - up where the cooperative links are of the same band as the links in the interference channel .in particular , characterizes the sum capacity of gaussian interference channels with in - band receiver cooperation to within 40 bits . our work , on the other hand ,is focused on the gaussian interference channel with orthogonal receiver cooperation .we propose a strategy achieving the symmetric capacity universally to within 3 bits in the symmetric set - up , regardless of channel parameters .the three - bit gap is the worst - case gap which can be loose in some regimes , and it is vanishingly small at high when compared to the capacity.the strategy consists of two parts : ( 1 ) the transmission scheme , describing how transmitters encode their messages , and ( 2 ) the cooperative protocol , describing how receivers exchange information and decode messages . for transmission ,both transmitters use superposition coding with a simple power - split configuration , which is the same as that in the case without cooperation , to encode messages . for the cooperative protocol , it is appealing to apply the decode - forward or compress - forward schemes , originally proposed in for the relay channel , like most works dealing with more complicated networks , including , , , , , etc .it turns out neither compress - forward nor decode - forward achieves capacity to within a constant number of bits universally for the problem at hand . on the other hand , , , and that the conventional compress - forward scheme may be improved by the destination directly decoding the sender s message instead of requiring to first decode the quantized signal of the relay .we use such an improved compress - forward scheme as our cooperative protocol .each receiver first quantizes its received signal at an appropriate distortion , bins the quantization codeword and sends the bin index to the other receiver .each receiver then decodes its own information based on its own received signal and the received bin index .it turns out that this simple `` one - round '' cooperative protocol is sufficient to achieve within a constant gap to the symmetric capacity in the symmetric case . in the general asymmetric channel, it turns out that this simple protocol is not sufficient while a more sophisticated `` two - round '' protocol is .the gaussian interference channel with conferencing decoders is depicted in fig .[ fig_chmodel ] .the transmitter - receiver links are modeled as the _ normalized _ gaussian interference channel : where the additive noise processes \} ] to denote time indices .transmitter intends to convey message to receiver by encoding it into a block codeword \}_{n=1}^n ] is only a function of ,\ldots , y_2[n-1 ] , u_{12}[1 ] , \ldots , u_{12}[n-1]\} ] is only a function of ,\ldots , y_1[n-1 ] , u_{21}[1 ] , \ldots , u_{21}[n-1]\}$ ] .we focus on the symmetric set - up , namely , , , and . for the symmetric set - up, a natural performance measure is the symmetric capacity , where is the capacity region .   in our model ,note that arbitrarily large number of rounds are allowed for conferencing among receivers .remarkably , with the proposed strategy , one - round conference is sufficient to achieve to within constant number of bits universally .the strategy proposed in this section consists of three basic ingredients : _ superposition coding _ at transmitters , _ quantize - binning _ for relaying , and a decoder keeping track of the codebook structure when figuring out quantization codewords . due to space constraint, we give an outline ._ superposition coding _ :  for each transmitter , it splits its own message into common and private ( sub-)messages . each common message is aimed at both receivers , while each private one is aimed at its own receiver . each message is encoded into a gaussian random codeword with certain power . as points out , since the private signal is undesired at the unintended receiver , a reasonable configuration is to make the private interference at or below the noise level so that it does not cause much damage and can still convey additional information in the direct link if it is stronger than the cross link . when the interference is stronger than the desired signal , simply set the whole message to be common.  _ quantize - binning _ :  upon receiving its signal from the transmitter - receiver link , each receiver does not decode messages immediately .instead , each receiver , serving as a relay , first quantizes its signal by a pre - generated gaussian quantization codebook with certain distortion , and then sends out a bin index determined by a pre - generated binning function .how should we set the distortion ?note that both its own private signal and the noise it encounters are not of interest to the other receiver .therefore , a natural configuration is to set the distortion level equal to _ the maximum of noise power and private signal power level_.  _ decoding _ :  after retrieving the receiver - cooperative side information , that is , the bin index , the receiver decodes the two common messages and its own private message , by searching in transmitters codebooks for a codeword triple ( indexed by the two common messages and the user s own private message ) that is jointly typical with its received signal and some quantization point ( codeword ) in the given bin .if there is no such unique codeword triple , it declares an error .note that the main difference between our cooperative protocol and the conventional compress - forward with gaussian vector quantization lies in the _ decoding _ procedure and the chosen distortion . in the conventional gaussian compress - forward ,the decoder first searches in the bin for one quantization codeword that is jointly typical with its received signal from its own transmitter _only _ , assuming that the two received signals are jointly gaussian. this may not be true since a single user may not transmit at the capacity in its own link , which results in holes " in signal space . as a consequence, this scheme may not utilize the dependency of two received signals well and cause larger distortions .our scheme , on the other hand , utilizes the dependency in a better way by _jointly _ deciding the quantization codeword and the message triple , consequently allows smaller distortions , and is able to reveal the beneficial side information to the other receiver .we give an example to illustrate the above observations . in this example channel ,set to be 2/3 of in db scale , that is , .besides , set . to better convey the key ideas , we make use of the _ linear deterministic channel _ ( ldc ) proposed in . the corresponding channel is depicted in fig .[ fig_examples ] .the bits and can be viewed as the binary expansions of the transmitted signals .note that in this example , one bit in the ldc corresponds to in the gaussian channel . as a baseline , without cooperationthe optimal sum rate is 4 bits in the ldc . with one - bit cooperation in each direction in the ldc ,the optimal sum rate is 5 bits .the scheme is depicted in fig . [fig_examples].(a ) .  from its corresponding ldc, one can see that the two received signals of the gaussian channel , , are not jointly gaussian .the reason is that , suppose they are jointly gaussian , the conditional distribution of given should be marginally gaussian . as fig .[ fig_examples ] suggests , however , conditioning on receiver 1 s signal results in a hole at the second level of receiver 2 s signal , which was occupied by .therefore , transmitter 2 s common codebook is not dense enough to make the conditional distribution of given marginally gaussian .  now , suppose compress - forward assuming joint gaussianity of the received signals is used for receivers to cooperate .this is a standard approach to evaluate achievable rates of gaussian channels using compress - forward in the literature .the incorrect assumption results in larger quantization distortions , as depicted in fig . [ fig_examples].(b ) .the information sent from receiver 1 to receiver 2 , , is _ redundant _ , and can not help mitigate interference .hence , the achievable sum rate is 4 bits ( 3 bits for user 1 and 1 bit for user 2 ) , which is the same as that without cooperation and is one bit less than the optimal performance .recall that 1 bit in the ldc corresponds to in the gaussian channel , therefore the performance loss is unbounded as .our scheme is very similar to _ extended hash - and - forward _ proposed in , in which it is pointed out that the scheme has no advantage over the conventional compress - forward in a single - source single - relay setting .due to the above mentioned issues , however , we recognize in our problem where the channel consists of two source - destination pairs and two relays , the scheme has an unbounded advantage over the conventional compress - forward in certain regimes .due to space constraint , we give the following coding theorem without proof .let and denote the rates for user s common message and private message respectively , for .  the rate tuple satisfying the following constraints are achievable : + : where , and is the common codebook generating random variable for . where is the quantization codebook generating random variable , and , independent of everything else . is the quantization distortion at receiver 2 . :the above constraints with index 1 " and 2 " exchanged .  in the rest of this paper , we focus on the symmetric set - up . besides , for simplicity we assume the typical case where and .we defer the full treatment of general asymmetric set - up in a subsequent paper .[ lem_achievable ] when , ,\\ \frac{1}{2}\big [ \log{\left(}1 + 2{\mathsf{snr}}+2{\mathsf{inr}}+|h_{11}h_{22}-h_{12}h_{21}|^2 { \right)}-1\big ] \end{subarray}\rbp\end{aligned}\ ] ] is achievable . when , ,\\ \frac{1}{2}\big [ \log{\left(}1 + 2{\mathsf{snr}}+2{\mathsf{inr}}+|h_{11}h_{22}-h_{12}h_{21}|^2 { \right)}-3\big ] \end{subarray}\rbp\end{aligned}\ ] ] is achievable .next , we have outer bounds for symmetric capacity :  [ thm_symoutbd ] with   with lemma [ lem_achievable ] and [ thm_symoutbd ] , we establish the following theorem :  [ thm_constgap ] the strategy can achieve the symmtric capacity to within 3 bits ; namely , degrees of freedom ( g.d.o.f . )characterization , originally proposed in , is an asymptotic capacity characterization in high regime . for our problem , it is appealing to define a similar notion for characterizing the high- asymptotic performance , in the following way : let , , and define the number of generalized degrees of freedom per user as if the limit exists . with fixed and , however , there are certain channel realizations under which has different values and hence the limit does not exist .this happens when , where the phases of the channel gains matter both in inner and outer bounds .in particular , its value can depend on whether the system mimo matrix is well - conditioned or not .  instead of claiming that the limit ( [ eq_dof ] ) exists for _ all _ channel realizations, we pose a reasonable distribution , namely , i.i.d .uniform distribution , on the phases , show that the limit exists _ almost surely _ , and define the limit to be the number of _ generalized degrees of freedom _ per user .details are omitted here due to space constraint .in particular , is the same as the symmetric capacity normalized by the interference - free capacity per user in the corresponding linear deterministic channel ( ldc ) except for .  now that the number of g.d.o.f. is well - defined , we can give the following theorem :  [ thm_dof ] we have a direct consequence from lemma [ thm_symoutbd ] and theorem [ thm_constgap ] :   numerical plots for g.d.o.f .are given in fig .[ fig_dof ] .we observe that at different values of , the gain from cooperation varies . by investigating the g.d.o.f . , we conclude that at high , when is below 50% of in db scale , one - bit cooperation per direction buys roughly one - bit gain per user until full receiver cooperation performance is reached , while when is between 67% and 200% of in db scale , one - bit cooperation per direction buys roughly half - bit gain per user until saturation .the fundamental behavior of the gain from receiver cooperation is explained in the rest of this section , by looking at two particular points : and .furthermore , we use the linear deterministic channel ( ldc ) for illustration .  at , the plot of versus is given in fig .[ fig_regimes].(a ) .the slope is 1 until full receiver cooperation performance is reached , implying that one - bit cooperation buys one more bit per user .we look at a particular point and use its corresponding ldc ( fig . [ fig_regimes].(b ) ) to provide insights .note that 1 bit in the ldc corresponds to in the gaussian channel , and since , in the corresponding ldc each receiver is able to sent one - bit information to the other . without cooperation ,the optimal way is to turn on bits not causing interference , that is , the _ private _bits .we can not turn on more bits without cooperation since it causes collisions , for example , at the fourth level of receiver 2 if we turn on bit . now with receiver cooperation , we want to support two more bits .note that prior to turning on , there are holes " left in receiver signal spaces , and turning on each of these bits only causes one collision at one receiver .therefore , we need 1 bit in each direction to resolve the collision at each receiver. we can achieve 3 bits per user in the corresponding ldc and in the gaussian channel .we can not turn on more bits in the ldc since it causes collisions while no cooperation capability is left .  at , the plot of versus given in fig .[ fig_regimes].(c ) .the slope is until full receiver cooperation performance is reached , implying that two - bit cooperation buys one more bit per user .we look at a particular point and use its corresponding ldc ( fig . [ fig_regimes].(d ) ) to provide insights .note that now 1 bit in the ldc corresponds to in the gaussian channel , and since , in the corresponding ldc each receiver is able to sent one - bit information to the other . without cooperation ,the optimal way is to turn on bits .we can not turn on more bits without cooperation since it causes collisions , for example , at the second level of receiver 2 if we turn on bit . now with receiver cooperation , we want to support one more bit .note that prior to turning on , there are no holes " left in receiver signal spaces , and turning on causes collisions at _ both _ receivers .therefore , we need 2 bits in total to resolve collisions at both receivers. we can achieve 5 bits in total in the corresponding ldc and in the gaussian channel .we can not turn on more bits in the ldc since it causes collision while no cooperation capability is left .  from above examples and illustrations , we see that whether _one cooperation bit buys one more bit _ or _ two cooperation bits buy one more bit _ depends on whether there are holes " in receiver signal spaces before increasing data rates .the holes " play a central role not only in why the conventional compress - forward is suboptimal in certain regimes , as mentioned in the previous section , but also in the fundamental behavior of the gain from receiver cooperation .we notice that in , there is a similar behavior about the gain from cooperation as discussed in section 3.2 . of .we conjecture that the behavior can be explained via the concept of holes " as well .in this paper , we propose a one - round scheme achieving the symmetric capacity to within 3 bits . the proposed one - round scheme , however , is not sufficient to achieve the capacity region to within a constant number of bits in general . as an example , consider the ldc in fig .[ fig_asymm ] . if receiver 2 quantizes at it private signal level , it can only forward to receiver 1 and achieves up to 2 bits. on the other hand , if receiver 2 first decodes and then forwards to receiver 1 , it achieves bits . in , this problem with general asymmetric set - upis investigated .we implement a two - round strategy and show that it can achieve the capacity region universally to within 2 bits per user .o. simeone , d. gndz , h. v. poor , a. j. goldsmith , and s. shamai , `` compound multiple access channels with partial cooperation , '' _ ieee transactions on information theory _55 , pp . 24252441 , june 2009 .w. yu and l. zhou , `` gaussian z - interference channel with a relay link : achievability region and asymptotic sum capacity , '' _ submitted to ieee transactions on information theory _ ,september 2008 , http://www.comm.utoronto.ca/~weiyu/z_relay.pdf .a. s. avestimehr , s. n. diggavi , and d. n. c. tse , `` wireless network information flow : a deterministic approach , '' _ submitted to ieee transactions on information theory _ ,http://arxiv.org/abs/0906.5394 2009 .

Abstract: interference is a major issue that limits the performance in wireless networks , and cooperation among receivers can help mitigate interference by forming distributed mimo systems . the rate at which receivers cooperate , however , is limited in most scenarios . how much interference can one bit of receiver cooperation mitigate ? in this paper , we study the two - user gaussian interference channel with conferencing decoders to answer this question in a simple setting . we characterize the fundamental gain from cooperation : at high , when is below 50% of in db scale , one - bit cooperation per direction buys roughly one - bit gain per user until full receiver cooperation performance is reached , while when is between 67% and 200% of in db scale , one - bit cooperation per direction buys roughly half - bit gain per user . the conclusion is drawn based on the approximate characterization of the symmetric capacity in the symmetric set - up . we propose strategies achieving the symmetric capacity universally to within 3 bits . the strategy consists of two parts : ( 1 ) the transmission scheme , where superposition encoding with a simple power split is employed , and ( 2 ) the cooperative protocol , where quantize - binning is used for relaying .