Effective missing data prediction for collaborative filtering

ABSTRACT

Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the user-item matrix is quite sparse, which directly leads to inaccurate recommendations. This paper focuses the memory-based collaborative filtering problems on two crucial factors: (1) similarity computation between users or items and (2) missing data prediction algorithms. First, we use the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items. Second, we propose an effective missing data prediction algorithm, in which information of both users and items is taken into account. In this algorithm, we set the similarity threshold for users and items respectively, and the prediction algorithm will determine whether predicting the missing data or not. We also address how to predict the missing data by employing a combination of user and item information. Finally, empirical studies on dataset MovieLens have shown that our newly proposed method outperforms other state-of-the-art collaborative filtering algorithms and it is more robust against data sparsity.

REFERENCES

• 1: Justin Basilico , Thomas Hofmann, Unifying collaborative and content-based filtering, Proceedings of the twenty-first international conference on Machine learning, p.9, July 04-08, 2004, Banff, Alberta, Canada [doi>10.1145/1015330.1015394]
• 2: J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI, 1998.
• 3: John Canny, Collaborative filtering with privacy via factor analysis, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564419]
• 4: M. Claypool, A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin. Combining content-based and collaborative filters in an online newspaper. In Proc. of SIGIR, 1999.
• 5: 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]
• 6: Jon Herlocker , Joseph A. Konstan , John Riedl, An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms, Information Retrieval, v.5 n.4, p.287-310, October 2002 [doi>10.1023/A:1020443909834]
• 7: Jonathan L. Herlocker , Joseph A. Konstan , Al Borchers , John Riedl, An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.230-237, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312682]
• 8: Thomas Hofmann, Collaborative filtering via gaussian probabilistic latent semantic analysis, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860483]
• 9: Thomas Hofmann, Latent semantic models for collaborative filtering, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.89-115, January 2004 [doi>10.1145/963770.963774]
• 10: Rong Jin , Joyce Y. Chai , Luo Si, An automatic weighting scheme for collaborative filtering, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009051]
• 11: A. Kohrs and B. Merialdo. Clustering for collaborative filtering applications. In Proc. of CIMCA, 1999.
• 12: 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]
• 13: Matthew R. McLaughlin , Jonathan L. Herlocker, A collaborative filtering algorithm and evaluation metric that accurately model the user experience, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009050]
• 14: David M. Pennock , Eric Horvitz , Steve Lawrence , C. Lee Giles, Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach, Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, p.473-480, June 30-July 03, 2000
• 15: Jasson D. M. Rennie , Nathan Srebro, Fast maximum margin matrix factorization for collaborative prediction, Proceedings of the 22nd international conference on Machine learning, p.713-719, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102441]
• 16: 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]
• 17: 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]
• 18: 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]
• 19: L. Si and R. Jin. Flexible mixture model for collaborative filtering. In Proc. of ICML, 2003.
• 20: L. H. Ungar and D. P. Foster. Clustering methods for collaborative filtering. In Proc. Workshop on Recommendation System at the 15th National Conf. on Artificial Intelligence, 1998.
• 21: Jun Wang , Arjen P. de Vries , Marcel J. T. Reinders, Unifying user-based and item-based collaborative filtering approaches by similarity fusion, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148257]
• 22: Gui-Rong Xue , Chenxi Lin , Qiang Yang , WenSi Xi , Hua-Jun Zeng , Yong Yu , Zheng Chen, Scalable collaborative filtering using cluster-based smoothing, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076056]

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, data prediction, data sparsity, recommender system