Collaborative filtering for orkut communities: discovery of user latent behavior

Users of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective community recommendations in order to meet more users. In this paper, we investigate two algorithms from very different domains and evaluate their effectiveness for personalized community recommendation. First is association rule mining (ARM), which discovers associations between sets of communities that are shared across many users. Second is latent Dirichlet allocation (LDA), which models user-community co-occurrences using latent aspects. In comparing LDA with ARM, we are interested in discovering whether modeling low-rank latent structure is more effective for recommendations than directly mining rules from the observed data. We experiment on an Orkut data set consisting of 492, 104 users and 118, 002 communities. Our empirical comparisons using the top-k r...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: WenYen Chen, Jon-Chyuan Chu, Junyi Luan, Hongjie Bai, Yi Wang, Edward Y. Chang
• Keywords: Community Recommendation Task, Internet Technology, Latent Dirichlet Allocation, Low-rank Latent Structure, WWW 2009