Wen-Yen Chen
ABSTRACT
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.
- Open source parallel lda. http://code.google.com/p/plda/.Google Scholar
- R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of the 1993 ACM SIGMOD conference, pages 207--216, 1993. Google ScholarDigital Library
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proc. of the 20th VLDB conference, pages 487--499, 1994. Google ScholarDigital Library
- D. M. Blei and M. I. Jordan. Modeling annotated data. In Proc. of the 26th ACM SIGIR conference, pages 127--134, New York, NY, USA, 2003. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarDigital Library
- J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Communications of the ACM, 51(1):107--113, 2008. Google ScholarDigital Library
- T. L. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy Science, 101 (suppl. 1):5228--5235, April 2004.Google ScholarCross Ref
- W. Gropp, E. Lusk, and A. Skjellum. Using MPI-2: Advanced Features of the Message-Passing Interface. MIT Press, 1999. Google ScholarDigital Library
- J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov., 8(1):53--87, 2004. Google ScholarDigital Library
- K. Jarvelin and J. Kekalainen. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20(4):422--446, 2002. Google ScholarDigital Library
- Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proc. of the 14th ACM SIGKDD conference, pages 426--434, 2008. Google ScholarDigital Library
- H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Y. Chang. Pfp: parallel fp-growth for query recommendation. In Proc. of the 2008 ACM RecSys conference, pages 107--114, 2008. Google ScholarDigital Library
- A. McCallum, A. Corrada-Emmanuel, and X. Wang. The author-recipient-topic model for topic and role discovery in social networks: Experiments with enron and academic email. Technical report, Computer Science, University of Massachusetts Amherst, 2004.Google Scholar
- J. J. Sandvig, B. Mobasher, and R. Burke. Robustness of collaborative recommendation based on association rule mining. In Proc. of the 2007 ACM Recommender Systems conference, pages 105--112, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- M.-L. Shyu, C. Haruechaiyasak, S.-C. Chen, and N. Zhao. Collaborative filtering by mining association rules from user access sequences. In Proc. of the Web Information Retrieval and Integration workshop, pages 128--135, 2005. Google ScholarDigital Library
- M. Snir and S. Otto. MPI-The Complete Reference: The MPI Core. MIT Press, 1998. Google ScholarDigital Library
- E. Spertus, M. Sahami, and O. Buyukkokten. Evaluating similarity measures: a large-scale study in the orkut social network. In Proc. of the 11th ACM SIGKDD Conference, pages 678--684, 2005. Google ScholarDigital Library
- R. Srikant and R. Agrawal. Mining generalized association rules. Future Gener. Comput. Syst., 13(2-3):161--180, 1997. Google ScholarDigital Library
- M. Steyvers, P. Smyth, M. Rosen-Zvi, and T. Griffiths. Probabilistic author-topic models for information discovery. In Proc. of the 10th ACM SIGKDD Conference, pages 306--315, 2004. Google ScholarDigital Library
- R. Thakur, R. Rabenseinfer, and W. Gropp. Optimization of collective communication operations in MPICH. International Journal of High Performance Computing Applications, 19(1):49--66, 2005.Google ScholarDigital Library
Index Terms
- Collaborative filtering for orkut communities: discovery of user latent behavior
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