Wei Zeng
ABSTRACT
Up to now, more and more social media sites have started to allow their users to build the social relationships. Take the Last.fm for example (which is a popular music-sharing site), users can not only add each other as friends, but also join interest groups that include people with common tastes. Therefore, in this environment, users might be interested in not only receiving item recommendations, but also friend recommendations whom they might consider putting in the contact list, and group recommendations that they may consider joining in. To support such needs, in this paper, we propose a generalized framework that provides three different types of recommendation in a single system: recommending items, recommending groups and recommending friends. For each type of recommendation, we in depth investigated the algorithm impact of fusing other two information resources (e.g., user-item preferences and friendship to be fused for recommending groups), along with their combined effect. The experiment reveals the ideal fusion mechanism for this multi-output recommender, and validates the benefit of factorization model for fusing bipartite data (such as membership and user-item preferences) and the benefit of regularization model for fusing one mode data (such as friendship). Moreover, the positive effect of integrating similarity measure into the regularization model is identified via the experiment.
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Index Terms
- Heterogeneous data fusion via matrix factorization for augmenting item, group and friend recommendations
Recommendations
A unified framework for recommending items, groups and friends in social media environment via mutual resource fusion
Up to now, more and more online sites have started to allow their users to build the social relationships. Take the Last.fm for example (which is a popular music-sharing site), users can not only add each other as friends, but also join online interest ...
Item recommendation in collaborative tagging systems via heuristic data fusion
Collaborative tagging systems have been popular on the Web. However, information overload results in the increasing need for recommender services from users, and thus item recommendation has been one of the key issues in such systems. In this paper, we ...
Attributes coupling based matrix factorization for item recommendation
Recommender systems have attracted lots of attention since they alleviate the information overload problem for users. Matrix factorization is one of the most widely employed collaborative filtering techniques in the research of recommender systems due ...
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