ABSTRACT
Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Consequently, prediction quality can be poor. This paper re-formulates the memory-based collaborative filtering problem in a generative probabilistic framework, treating individual user-item ratings as predictors of missing ratings. The final rating is estimated by fusing predictions from three sources: predictions based on ratings of the same item by other users, predictions based on different item ratings made by the same user, and, third, ratings predicted based on data from other but similar users rating other but similar items. Existing user-based and item-based approaches correspond to the two simple cases of our framework. The complete model is however more robust to data sparsity, because the different types of ratings are used in concert, while additional ratings from similar users towards similar items are employed as a background model to smooth the predictions. Experiments demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendations.
- J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI, 1998. Google ScholarDigital Library
- J. Canny. Collaborative filtering with privacy via factor analysis. In Proc. of SIGIR, 1999. Google ScholarDigital Library
- M. Deshpande and G. Karypis. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst., 22(1):143--177, 2004. Google ScholarDigital Library
- K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval Journal, 4(2):133--151, July 2001. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proc. of SIGIR, 1999. Google ScholarDigital Library
- D. Hiemstra. Term-specific smoothing for the language modeling approach to information retrieval: the importance of a query term. In Proc. of SIGIR, pages 35--41, 2002. Google ScholarDigital Library
- T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Info. Syst., Vol 22(1):89--115, 2004. Google ScholarDigital Library
- Z. Huang, H. Chen, and D. Zeng. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst., 22(1):116--142, 2004. Google ScholarDigital Library
- R. Jin, J. Y. Chai, and L. Si. An automatic weighting scheme for collaborative filtering. In Proc. of SIGIR, 2004. Google ScholarDigital Library
- J. Kittler, M. Hatef, R. P. W. Duin, and J. Matas. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell., 20(3):226--239, 1998. Google ScholarDigital Library
- G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, Jan/Feb.:76--80, 2003. Google ScholarDigital Library
- D. M. Pennock, E. Horvitz, S. Lawrence, and C. Giles. Collaborative filtering by personality diagnosis: a hybrid memory and model based approach. In Proc. of UAI, 2000. Google ScholarDigital Library
- J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proc. of ICML, 2005. Google ScholarDigital Library
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In Proc. of ACM CSCW, 1994. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proc. of the WWW Conference, 2001. Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system -- a case study. In Proc. of ACM WebKDD Workshop, 2000.Google ScholarCross Ref
- L. Si and R. Jin. Flexible mixture model for collaborative filtering. In ICML, 2003.Google Scholar
- J. Wang, A. P. de Vries, and M. J. Reinders. A user-item relevance model for log-based collaborative filtering. In Proc. of ECIR06, London, UK, 2006. Google ScholarDigital Library
- G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. In Proc. of SIGIR, 2005. Google ScholarDigital Library
Index Terms
- Unifying user-based and item-based collaborative filtering approaches by similarity fusion
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