In this paper, we propose a probabilistic generative model, called unified model, which naturally unifies the ideas of social influence, collaborative filtering and content-based methods for item recommendation. To address the issue of hidden social influence, we devise new algorithms to learn the model parameters of our proposal based on expectation maximization (EM). In addition to a single-machine version of our EM algorithm, we further devise a parallelized implementation on the Map-Reduce framework to process two large-scale datasets we collect. Moreover, we show that the social influence obtained from our generative models can be used for group recommendation. Finally, we conduct comprehensive experiments using the datasets crawled from last.fm and whrrl.com to validate our ideas. Experimental results show that the generative models with social influence significantly outperform those without incorporating social influence. The unified generative model proposed in this paper obtains the best performance. Moreover, our study on social influence finds that users in whrrl.com are more likely to get influenced by friends than those in last.fm. The experimental results also confirm that our social influence based group recommendation algorithm outperforms the state-of-the-art algorithms for group recommendation.
. To explore how to utilize social influence for group recommendation, we provides an in-depth study and comparison on group recommendation techniques. Group recommendations have been designed for various domains such as web news pages @cite_27 , tourism @cite_26 , music @cite_31 @cite_14 , and TV programs and movies @cite_21 @cite_24 . In summary, two main approaches have been proposed for group recommendation @cite_12 . The first one creates an aggregated profile for a group based on its group members and then makes recommendations based on the aggregated group profile @cite_31 @cite_24 . The second approach the recommendation results from individual members into a single group recommendation list. In other words, recommendations (i.e., ranked item lists) for individual members are created independently and then aggregated into a joint group recommendation list @cite_1 , where the aggregation functions could be based on average or least misery strategies @cite_9 . Different from these proposed methods, our approach regenerates the process of how group members would their preferences and other members to reach the final decision. Evaluation from real datasets demonstrates a significant improvement over the proposed method using social influence over the traditional methods.
@cite_14 @cite_26 @cite_9 @cite_21 @cite_1 @cite_24 @cite_27 @cite_31 @cite_12
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Flytrap is a group music environment that knows its users' musical tastes and can automatically construct a soundtrack that tries to please everyone in the room. The system works by paying attention to what music people listen to on their computers. Users of the system have radio frequency ID badges that let the system know when they are nearby. Using the preference information it has gathered from watching its users, and knowledge of how genres of music interrelate, how artists have influenced each other, and what kinds of transitions between songs people tend to make, the 'virtual DJ' finds a compromise and chooses a song. The system tries to satisfy the tastes of people in the room, but it also makes a playlist that fits its own notion of what should come next. Once it has chosen a song, music is automatically broadcast over the network and played on the closest machine. Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of users with potentially competing interests is challenging in itself. In addition to this we must consider how to record and combine the preferences of many different users as they engage in simultaneous recommendation dialogs. In this paper we introduce a group recommender system that is designed to provide assistance to a group of friends trying the plan a skiing vacation. The system uses the DiamondTouch interactive tabletop to allow up to 4 users to simultaneously engage in parallel recommendation sessions and we describe how personal and shared profiles and interaction spaces can be managed to generate sets of recommendations for the individual as well as the group. Watching television tends to be a social activity. So, adaptive television needs to adapt to groups of users rather than to individual users. In this paper, we discuss different strategies for combining individual user models to adapt to groups, some of which are inspired by Social Choice Theory. In a first experiment, we explore how humans select a sequence of items for a group to watch, based on data about the individuals' preferences. The results show that humans use some of the strategies such as the Average Strategy (a.k.a. Additive Utilitarian), the Average Without Misery Strategy and the Least Misery Strategy, and care about fairness and avoiding individual misery. In a second experiment, we investigate how satisfied people believe they would be with sequences chosen by different strategies, and how their satisfaction corresponds with that predicted by a number of satisfaction functions. The results show that subjects use normalization, deduct misery, and use the ratings in a non-linear way. One of the satisfaction functions produced reasonable, though not completely correct predictions. According to our subjects, the sequences produced by five strategies give satisfaction to all individuals in the group. The results also show that subjects put more emphasis than expected on showing the best rated item to each individual (at a cost of misery for another individual), and that the ratings of the first and last items in the sequence are especially important. In a final experiment, we explore the influence viewing an item can have on the ratings of other items. This is important for deciding the order in which to present items. The results show an effect of both mood and topical relatedness. We present PolyLens, a new collaborative filtering recommender system designed to recommend items for groups of users, rather than for individuals. A group recommender is more appropriate and useful for domains in which several people participate in a single activity, as is often the case with movies and restaurants. We present an analysis of the primary design issues for group recommenders, including questions about the nature of groups, the rights of group members, social value functions for groups, and interfaces for displaying group recommendations. We then report on our PolyLens prototype and the lessons we learned from usage logs and surveys from a nine-month trial that included 819 users We found that users not only valued group recommendations, but were willing to yield some privacy to get the benefits of group recommendations Users valued an extension to the group recommender system that enabled them to invite non-members to participate, via email This paper overviews methods and techniques useful for building group-adaptive systems and presents an experience of building a system to give news adapted to different group of users in a public space. Starting from the analysis of limits of group modelling strategies and of problems to pass from a user modelling to a group modelling approach in the adaptation of system interaction, we suggest an update of the probabilistic group model to improve the interaction of groups of users with devices devoted to show news. In particular, we analyze the way to build a group model useful to improve the adaptation of a system that provides news on a video wall in a public space which is attended from a group of users with common interests. Environmental factors affecting shared spaces are typically designed to appeal to the broadest audiences they are expected to serve, ignoring the preferences of the people actually inhabiting the environment at any given time. Examples of such factors include the lighting, temperature, decor or music in the common areas of an office building. We have designed and deployed MUSICFX, a group preference arbitration system that allows the members of a fitness center to influence, but not directly control, the selection of music in a fitness center. We present a number of empirical results from our work with this intelligent environment: the results of a poll of fitness center members, a quantitative evaluation of the performance of a group preference arbitrator in a shared environment, and some interesting anecdotes about members’ experiences with the system.