Abstract
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on collaborating filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The most common approach to CF is based on neighborhood models, which originate from similarities between products or users. In this work we introduce a new neighborhood model with an improved prediction accuracy. Unlike previous approaches that are based on heuristic similarities, we model neighborhood relations by minimizing a global cost function. Further accuracy improvements are achieved by extending the model to exploit both explicit and implicit feedback by the users. Past models were limited by the need to compute all pairwise similarities between items or users, which grow quadratically with input size. In particular, this limitation vastly complicates adopting user similarity models, due to the typical large number of users. Our new model solves these limitations by factoring the neighborhood model, thus making both item-item and user-user implementations scale linearly with the size of the data. The methods are tested on the Netflix data, with encouraging results.
- Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 6, 734--749. Google ScholarDigital Library
- Ali, K. and van Stam, W. 2004. Tivo: making show recommendations using a distributed collaborative filtering architecture. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 394--401. Google ScholarDigital Library
- Bell, R. and Koren, Y. 2007a. Lessons from the Netflix Prize challenge. SIGKDD Explor. Newslet. 9, 2, 75--79. Google ScholarDigital Library
- Bell, R., Koren, Y., and Volinsky, C. 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 95--104. Google ScholarDigital Library
- Bell, R. M. and Koren, Y. 2007b. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE Computer Society, 43--52. Google ScholarDigital Library
- Bennett, J. and Lanning, S. 2007. The Netflix Prize. In Proceedings of the KDD Cup and Workshop.Google Scholar
- Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022. Google ScholarCross Ref
- Canny, J. 2002. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'02). ACM, New York, NY, 238--245. Google ScholarDigital Library
- Das, A. S., Datar, M., Garg, A., and Rajaram, S. 2007. Google news personalization: Scalable online collaborative filtering. In Proceedings of the 16th International Conference on World Wide Web (WWW'07). ACM, New York, NY, 271--280. Google ScholarDigital Library
- Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Inform. Sci. 41, 391--407.Google ScholarCross Ref
- Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Comm. ACM 35, 12, 61--70. Google ScholarDigital Library
- Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99). ACM, New York, NY, 230--237. Google ScholarDigital Library
- Herlocker, J. L., Konstan, J. A., and Riedl, J. 2000. Explaining collaborative filtering recommendations. In Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW'00). ACM, New York, NY, 241--250. Google ScholarDigital Library
- Hofmann, T. 2004. Latent semantic models for collaborative filtering. ACM Trans. Inform. Syst. 22, 1, 89--115. Google ScholarDigital Library
- Kim, D. and Yum, B.-J. 2005. Collaborative filtering based on iterative principal component analysis. Expert Syst. Appl. 28, 4, 823--830. Google ScholarDigital Library
- Koren, Y. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08). ACM, New York, NY, 426--434. Google ScholarDigital Library
- Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7, 1, 76--80. Google ScholarDigital Library
- Marlin, B. M., Zemel, R. S., Roweis, S., and Slaney, M. 2007. Collaborative filtering and the missing at random assumption. In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI).Google Scholar
- Oard, D. and Kim, J. 1998. Implicit feedback for recommender systems. In Proceedings of the AAAI Workshop on Recommender Systems. 31--36.Google Scholar
- Park, S.-T. and Pennock, D. M. 2007. Applying collaborative filtering techniques to movie search for better ranking and browsing. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07). ACM, New York, NY, 550--559. Google ScholarDigital Library
- Paterek, A. 2007. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop.Google Scholar
- Piatetsky, G. 2007. Interview with Simon Funk. SIGKDD Explor. Newsl. 9, 1, 38--40. Google ScholarDigital Library
- Salakhutdinov, R., Mnih, A., and Hinton, G. 2007. Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th International Conference on Machine Learning (ICML'07). ACM, New York, NY, 791--798. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW'01). ACM, New York, NY, 285--295. Google ScholarDigital Library
- Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. T. 2000. Application of dimensionality reduction in recommender system—a case study. In Proceedings of the ACM WebKDD Workshop.Google Scholar
- Takács, G., Pilászy, I., Németh, B., and Tikk, D. 2007. Major components of the gravity recommendation system. SIGKDD Explor. Newsl. 9, 2, 80--83. Google ScholarDigital Library
- Tintarev, N. and Masthoff, J. 2007. A survey of explanations in recommender systems. In Proceedings of the 22nd International Conference on Data Engineering Workshops, 801--810. Google ScholarDigital Library
- Wang, J., de Vries, A. P., and Reinders, M. J. T. 2006. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'06). ACM, New York, NY, 501--508. Google ScholarDigital Library
Index Terms
- Factor in the neighbors: Scalable and accurate collaborative filtering
Recommendations
Recommending new movies: even a few ratings are more valuable than metadata
RecSys '09: Proceedings of the third ACM conference on Recommender systemsThe Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by ...
Investigation of various matrix factorization methods for large recommender systems
NETFLIX '08: Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize CompetitionMatrix Factorization (MF) based approaches have proven to be efficient for rating-based recommendation systems. In this work, we propose several matrix factorization approaches with improved prediction accuracy. We introduce a novel and fast (semi)-...
Recommendation engine based on derived wisdom for more similar item neighbors
Collaborative filtering (CF) is a popular and widely accepted recommendation technique. CF is an automated form of word-of-mouth communication between like-minded or similar users. The search for these similar users as neighbors from a large user ...
Comments