Recommending Flickr groups with social topic model

Authors:
Jingdong Wang;Zhe Zhao;Jiazhen Zhou;Hao Wang;Bin Cui;Guojun Qi
Affiliations:
Microsoft Research Asia, Haidian, People's Republic of China 100080;Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, USA 48109;Department of Computer Science, Peking University, Beijing, People's Republic of China 100871;Beijing University of Posts and Telecommunications, Beijing, People's Republic of China 100876;State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, People's Republic of China 100191 and School of EECS, Peking University, Beijing, People's Republic of ...;Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, USA 61801-2300
Venue:
Information Retrieval
Year:
2012

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Abstract

The explosion of multimedia content in social media networks raises a great demand of developing tools to facilitate producing, sharing and viewing media content. Flickr groups, self-organized communities with declared common interests, are able to help users to conveniently participate in social media network. In this paper, we address the problem of automatically recommending groups to users. We propose to simultaneously exploit media contents and link structures between users and groups. To this end, we present a probabilistic latent topic model to model them in an integrated framework, expecting to jointly discover the latent interests for users and groups and simultaneously learn the recommendation function. We demonstrate the proposed approach on the dataset crawled from Flickr.com.