document: 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 recommendations metric show that LDA performs consistently better than ARM for the community recommendation task when recommending a list of 4 or more communities . However , for recommendation lists of up to 3 communities , ARM is still a bit better . We analyze examples of the latent information learned by LDA to explain this finding . To efficiently handle the large-scale data set , we parallelize LDA on distributed computers and demonstrate our parallel implementation 's scalability with varying numbers of machines .
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