Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approximations and standard factor models. MMMF can be formulated as a semi-definite programming (SDP) and learned using standard SDP solvers. However, current SDP solvers can only handle MMMF problems on matrices of dimensionality up to a few hundred. Here, we investigate a direct gradient-based optimization method for MMMF and demonstrate it on large collaborative prediction problems. We compare against results obtained by Marlin (2004) and find that MMMF substantially outperforms all nine methods he tested. <s> BIB001 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> This paper proposes a mobile ad hoc networking approach to detecting and querying events related to farm animals such as oestrus, animal diseases and decreased efficiency of pastures. This problem is essential because inattention to the health or welfare of the animals can lead to reduced productivity and death of valuable stock. The proposed approach guided by the identified requirements utilizes the available networking infrastructure but also works in a fully ad hoc infrastructure-less conditions. Data is acquired from the animal mounted devices using reliable multi hop wireless communication. Data can be also accessed from a portable device directly connecting to the sensor devices over wireless communication. We perform a simulation based evaluation of our approach showing that the proposed acquisition of data from the animal mounted devices is feasible even using energy efficient, low range, low bandwidth wireless communication. <s> BIB002 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit <s> BIB003 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> Combining archives with location-aware recommendation strategies to provide visitors with a more convenient way to browse exhibitions is an important research issue. An interactive learning-guide system, designed with Radio Frequency Identification (RFID) electronic tags and PDA, to assist the visitors in better understanding the exhibitions is proposed. Visitors obtain the exhibition-related information through a PDA with interactive RFID reading. Unlike traditional systems, this mobile guide is supplemented with data mining, information retrieval and location-awareness to help achieve the ideal format for interactive usage. The system utilizes the user profiles to analyze individuals' viewing records by employing collaborative filtering and Apriori-like algorithms to create recommended viewing rules for visitors. In considering the system would recommend the exhibitions in different sections to the visitors, the system also provides the directory map for visitors to easily locate the recommended exhibitions and minimize the difficulty of finding the recommended exhibitions. The architecture and the methodology used in the design system are given and comparisons with other work are presented in the paper. <s> BIB004 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> There are many activities that people prefer/opt out attending and these events are announced for attracting people. An intelligent recommendation system can be used in a social networking site in order to recommend people according to content and collaboration assessment. This study is an effort to recommend events to users within a social networking site. It can be any networking environment. We have used social environment that has been designed as a facebook application. Our application has also been integrated with several web sites. System collects event data from several related web sites either by using web services or web scraping. It also permits users rating events they have attended or planned. Given the social network between people, system tries to recommend upcoming events to users. For this purpose a combination of content based and collaborative filtering has been used. We have also taken geographical location info and social concept of an event. <s> BIB005 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> We present a recommender systems approach to conference paper assignment, i.e., the task of assigning paper submissions to reviewers. We address both the modeling of reviewer-paper preferences (which can be cast as a learning problem) and the optimization of reviewing assignments to satisfy global conference criteria (which can be viewed as constraint satisfaction). Due to the paucity of preference data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn reviewer-paper preference models. Our models are evaluated not just in terms of prediction accuracy but in terms of end-assignment quality. Using a linear programming-based assignment optimization, we show how our approach better explores the space of unsupplied assignments to maximize the overall affinities of papers assigned to reviewers. We demonstrate our results on real reviewer bidding data from the IEEE ICDM 2007 conference. <s> BIB006 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a query-document or document-document pair to a ranking score. Dealing with polynomial models on word features is computationally challenging. We propose a low-rank (but diagonal preserving) representation of our polynomial models to induce feasible memory and computation requirements. We provide an empirical study on retrieval tasks based on Wikipedia documents, where we obtain state-of-the-art performance while providing realistically scalable methods. <s> BIB007 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> We demonstrate a method for collaborative ranking of future events. Previous work on recommender systems typically relies on feedback on a particular item, such as a movie, and generalizes this to other items or other people. In contrast, we examine a setting where no feedback exists on the particular item. Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and dislikes. We examine the topic of unseen item recommendation through a user study of academic (scientific) talk recommendation, where we aim to correctly estimate a ranking function for each user, predicting which talks would be of most interest to them. Then by decomposing user parameters into shared and individual dimensions, we induce a similarity metric between users based on the degree to which they share these dimensions. We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation. Finally, to further reduce the need for explicit user feedback, we suggest an active learning approach for eliciting feedback and a method for incorporating available implicit user cues. <s> BIB008 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments. <s> BIB009 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> Providing valuable recommendations in the DVR domain is quite straightforward when enough information about users and/or contents is known. In this work, we discuss the possibility of recommending future live events without knowing anything else but past user programmed recording schedules. <s> BIB010 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts, recommender systems can now identify patterns in people’s movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location? To answer this question, we carry out a study of the relationship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events. <s> BIB011 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> Eliciting requirements on-site is challenging as analysts have to simultaneously observe the environment, interact with people and operate RE tools. We have been exploring the use of context-aware technologies to provide better guidance and support for on-site analysts. Our context aware Mobile Scenario Presenter tool guides analysts by automatically highlighting scenario events relevant to the currently observed work task. <s> BIB012 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> This short paper deals with the problem of deriving personalized recommendations for daily sightseeing itineraries for tourists visiting any destination. Our approach considers selected places of interest that a traveller would potentially wish to visit and derives a near-optimal itinerary for each day of visit; the places of potential interest are selected based on stated or implied user preferences. Herein, we propose a heuristic solution to this problem and discuss its implementation aspects. <s> BIB013 </s> Mobile Multimedia Recommendation in Smart Communities: A Survey <s> B. Recommendation in Mobile Event Guide <s> The current growth of mobile devices enabled with GPS receptors, cameras, accelerometers, and Internet access offers new opportunities for new mobile ubiquitous multimedia applications. Such applications help local communities to share resources using geo-location, interactive mapping, augmented reality, and several data representations. This paper presents CityEvents, a mobile solution that provides real time awareness-based information of local events and news. The presented ubiquitous system is a simple and easy to use event guide, providing location-awareness and content-awareness ordering data to the user. The proposal was evaluated and demonstrated in iPhone devices and it is ready for use. <s> BIB014
Social activities are occurring all over the world as an important part of life and an activity imminently requires answering the critical questions such as "what", "how", "where", "when", "why" and "who". Even if it is hard to give information and recommend events that may interest people regardless of the above questions, systems and methods have been developed for selecting and recommending events to a user of media content based on information known about the user in the community, including the user's interests, consumption history and preferences. An intelligent recommendation system can be used in a social networking site in order to recommend people according to content and collaboration assessment. The system further includes an event database containing information about upcoming events. When a user interacts with the system, such as to access a media content item, the user is notified of one or more events based on the user's history. In this way users are automatically provided with updated event recommendations based on the known interests of the user, without the need for the user to subscribe to an event recommendation service and maintain the subscription to accurately reflect the user's current interests. By the time one can expect user feedback on a specific event, that event is no longer relevant: an event recommendation system therefore has to recommend items for which no explicit feedback exists. This distinguishes events from other information items, like movies, for which some user feedback is directly available and continues to be useful. In general, one can approach event recommendation using existing content-based methods, relating event descriptions to user preferences. However, the quality of content-based recommendation is often highly dependent on the amount of user feedback available. We expect feedback about events to be relatively scarce; events are often topically diverse and new events may have low similarity to prior events. Based on these, Kayaalp et al. BIB005 recommended events to users within a social networking site. It can be any networking environment. They used a social environment that has been designed similarly to Facebook. Their application has also been integrated with several social networking sites, like Twitter or LinkedIn. Their system also permits users rating events they have attended, organized or planned. Given the social network between people, the system tries to recommend upcoming events to users. For this purpose, they used a combination of content-based and collaborative filtering. The work in BIB008 considered a recommendation system in which the information items are events. The authors pointed out that event recommendation has various potential applications. Park et al. BIB009 studied a location-tracking system, which records the user's whereabouts within a geographical region or inside a building. Given location coordinates, it is desirable to provide users with personalized location-based services. The study in BIB006 exploits collaborative filtering to construct a set of reviewing assignments for conferences in response to a primary input of paper-reviewer `bids' expressing interest or disinterest of reviewers in specific papers. While previous works apply collaborative filtering in "traditional" settings, based on common pools of items and users, they solved a different problem, where the sets of known items and the items for which recommendation takes place are distinct. In particular, their approach can be seen as an extension of event recommendation in BIB001 where, in contrast to the more common user-item rating scenario, the authors seek to rank future events on the basis of their word content. As a result, their problem formulation is closely related to the low-rank document retrieval method of BIB007 . The task of recommending live events, such as TV shows, has been already investigated in the past years. Basso et al. BIB010 showed how to recommend live events to users without any knowledge about the broadcast content and user's interests. Recommendations can be given both globally and personally. It is important to underline that the most popular events are easier to predict since users tend to naturally focus on them, even without any specific suggestion. On the contrary, granting a high novelty in personal recommendations is a more challenging goal due to the reduced amount of explicit information. Using mobile phone data, Quercia et al. BIB011 have been able to infer which events had been attended and have considered the attendance as implicit user feedback. In studying different ways of recommending events, they found that recommending nearby events is ill-suited to effectively recommend places of interest in a city. In contrast, recommending events that are popular among residents of the area is more beneficial. They put forward the idea that, by sharing attendance at social events, people are able to receive quality recommendations of future events. To avoid using explicit inputs as context sources, Cimino et al.  proposed and designed an approach based on the emergent paradigm BIB003 for detecting events. They designed a form of collaborative situation awareness considering an important class of events. In this scenario, they also discussed collaborative multi-agent scheme for the detection of such events, structured into three levels of information processing. The first level is managed by a stigmergic paradigm  , in which marking agents leave marks in the environment that is associated to users' positioning. The work in BIB014 CityEvents, a mobile solution that provides real time awareness based information of local events and news. The presented ubiquitous system is a simple and easy to use event guide, providing a location-awareness and content-awareness ordering data to the user. The proposal was evaluated and demonstrated in i-Phone devices and it is ready for use. The study in BIB002 proposes a mobile ad hoc networking approach for detecting and querying events related to farm animals such as oestrus, animal diseases and decreased efficiency of pastures. This problem is essential because inattention to the health or welfare of the animals can lead to reduced productivity and death of valuable stock. The proposed approach guided by the identified requirements, utilizes the available networking infrastructure but also works in a fully ad hoc infrastructure-less condition. An interactive learning-guide system, designed with Radio Frequency Identification (RFID) electronic tags and PDA, to assist the visitors in better understanding the exhibitions is proposed in BIB004 . Visitors obtain the exhibition-related information through a PDA with interactive RFID reading. Unlike traditional systems, this mobile guide is supplemented with data mining, information retrieval and location-awareness to help achieve the ideal format for interactive usage. The system utilizes the user profiles to analyze individuals' viewing records by employing collaborative filtering and Apriori-like algorithms to create recommended viewing rules for visitors. The study in  solved a novel problem of route recommendation which guides the user through a series of locations. The recommendation is made by matching the user's current route with the set of popular route patterns. Sequential pattern mining methods are used to extract the popular route patterns from a large set of historical route records from previous users. As an application with real requirements, the Bar Tour Guide is used to demonstrate the effectiveness of the proposed solution. Eliciting requirements on-site is challenging as analysts have to simultaneously observe the environment, interact with people and operate requirements engineering tools. Seyff et al. BIB012 explored the use of context-aware technologies to provide better guidance and support for on-site analysts. The context aware mobile scenario presents tool guides to analysts by automatically highlighting scenario events relevant to the currently observed work task. The study in BIB013 deals with the problem of deriving personalized recommendations for daily sightseeing itineraries for tourists visiting any destination. Their approach considers selected places of interest that a traveler would potentially wish to visit and derives a near-optimal itinerary for each day of visit; the places of potential interest are selected based on stated or implied user preferences. Therein, the authors proposed a heuristic solution to this problem and discussed its implementation.