Multi-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects

Details Multi-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects Multi-Output Recommender: Items, Groups and Friends, and Their Mutual Contributing Effects

2012-04-10

arxiv.org/abs/1204.2032v2

Computer Science

Information Retrieval

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Scientific paper

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, users could 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 getting suggestion of friends whom they might put in the contact list, and recommendation of interest groups that they could consider to join. 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 investigate the respective roles of other two information resources (e.g., user-item preferences and friendship for recommending groups), along with their combined effect. The experiment reveals the ideal fusion mechanism for the multi-output recommendation, and validates the benefit of factorization model for fusing bipartite data (such as membership and user-item preferences) and the impact of regularization model on incorporating 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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