A random walk method for alleviating the sparsity problem in collaborative filtering

ABSTRACT

Collaborative Filtering is one of the most widely used approaches in recommendation systems which predicts user preferences by learning past user-item relationships. In recent years, item-oriented collaborative filtering methods came into prominence as they are more scalable compared to user-oriented methods. Item-oriented methods discover item-item relationships from the training data and use these relations to compute predictions. In this paper, we propose a novel item-oriented algorithm, Random Walk Recommender, that first infers transition probabilities between items based on their similarities and models finite length random walks on the item space to compute predictions. This method is especially useful when training data is less than plentiful, namely when typical similarity measures fail to capture actual relationships between items. Aside from the proposed prediction algorithm, the final transition probability matrix computed in one of the intermediate steps can be used as an item similarity matrix in typical item-oriented approaches. Thus, this paper suggests a method to enhance similarity matrices under sparse data as well. Experiments on MovieLens data show that Random Walk Recommender algorithm outperforms two other item-oriented methods in different sparsity levels while having the best performance difference in sparse datasets.

REFERENCES

• 1: Movielens data set. Available at http://www.grouplens.org/node/73#attachments, Nov. 2007.
• 2: Gediminas Adomavicius , Alexander Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, IEEE Transactions on Knowledge and Data Engineering, v.17 n.6, p.734-749, June 2005 [doi>10.1109/TKDE.2005.99]
• 3: Baruch Awerbuch , Boaz Patt-Shamir , David Peleg , Mark Tuttle, Improved recommendation systems, Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms, January 23-25, 2005, Vancouver, British Columbia
• 4: Yossi Azar , Amos Fiat , Anna Karlin , Frank McSherry , Jared Saia, Spectral analysis of data, Proceedings of the thirty-third annual ACM symposium on Theory of computing, p.619-626, July 2001, Hersonissos, Greece [doi>10.1145/380752.380859]
• 5: J. K. Badrul Sarwar, George Karypis and J. Riedl. Application of dimensionality reduction in recommender system a case study. In WebKDD-2000 Workshop, 2000.
• 6: Robert Bell , Yehuda Koren , Chris Volinsky, Modeling relationships at multiple scales to improve accuracy of large recommender systems, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA [doi>10.1145/1281192.1281206]
• 7: J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. pages 43--52, 1998.
• 8: Mukund Deshpande , George Karypis, Item-based top-N recommendation algorithms, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.143-177, January 2004 [doi>10.1145/963770.963776]
• 9: Petros Drineas , Iordanis Kerenidis , Prabhakar Raghavan, Competitive recommendation systems, Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, May 19-21, 2002, Montreal, Quebec, Canada [doi>10.1145/509907.509922]
• 10: David Goldberg , David Nichols , Brian M. Oki , Douglas Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, v.35 n.12, p.61-70, Dec. 1992 [doi>10.1145/138859.138867]
• 11: Ken Goldberg , Theresa Roeder , Dhruv Gupta , Chris Perkins, Eigentaste: A Constant Time Collaborative Filtering Algorithm, Information Retrieval, v.4 n.2, p.133-151, July 2001 [doi>10.1023/A:1011419012209]
• 12: Zan Huang , Wingyan Chung , Thian-Huat Ong , Hsinchun Chen, A graph-based recommender system for digital library, Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries, July 14-18, 2002, Portland, Oregon, USA [doi>10.1145/544220.544231]
• 13: Z. D. Huang, Z. and H. Chen. A link analysis approach to recommendation with sparse data. In Americas Conference on Information Systems, New York, NY, 2004.
• 14: Jon Kleinberg , Mark Sandler, Convergent algorithms for collaborative filtering, Proceedings of the 4th ACM conference on Electronic commerce, p.1-10, June 09-12, 2003, San Diego, CA, USA [doi>10.1145/779928.779929]
• 15: Jon Kleinberg , Mark Sandler, Using mixture models for collaborative filtering, Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, June 13-16, 2004, Chicago, IL, USA [doi>10.1145/1007352.1007439]
• 16: Ravi Kumar , Prabhakar Raghavan , Sridhar Rajagopalan , Andrew Tomkins, Recommendation Systems: A Probabilistic Analysis, Proceedings of the 39th Annual Symposium on Foundations of Computer Science, p.664, November 08-11, 1998
• 17: Greg Linden , Brent Smith , Jeremy York, Amazon.com Recommendations: Item-to-Item Collaborative Filtering, IEEE Internet Computing, v.7 n.1, p.76-80, January 2003 [doi>10.1109/MIC.2003.1167344]
• 18: L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.
• 19: Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, United States [doi>10.1145/192844.192905]
• 20: Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372071]
• 21: Upendra Shardanand , Pattie Maes, Social information filtering: algorithms for automating “word of mouth”, Proceedings of the SIGCHI conference on Human factors in computing systems, p.210-217, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223931]
• 22: L. Ungar and D. Foster. Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park California, 1998

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Algorithms, Experimentation, Performance

Keywords:
collaborative filtering, random walk, sparsity