Abstract
Many recommendation systems suggest items to users by utilizing the techniques of collaborative filtering (CF) based on historical records of items that the users have viewed, purchased, or rated. Two major problems that most CF approaches have to contend with are scalability and sparseness of the user profiles. To tackle these issues, in this paper, we describe a CF algorithm alternating-least-squares with weighted-λ -regularization (ALS-WR), which is implemented on a parallel Matlab platform. We show empirically that the performance of ALS-WR (in terms of root mean squared error (RMSE)) monotonically improves with both the number of features and the number of ALS iterations. We applied the ALS-WR algorithm on a large-scale CF problem, the Netflix Challenge, with 1000 hidden features and obtained a RMSE score of 0.8985, which is one of the best results based on a pure method. In addition, combining with the parallel version of other known methods, we achieved a performance improvement of 5.91% over Netflix’s own CineMatch recommendation system. Our method is simple and scales well to very large datasets.
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References
The Hadoop Project, http://lucene.apache.org/hadoop/
Netflix CineMatch, http://www.netflix.com
Balabanovi, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)
Bell, R., Koren, Y., Volinsky, C.: The bellkor solution to the netflix prize. Netflix Prize Progress Award (October 2007), http://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf
Bell, R., Koren, Y., Volinsky, C.: Modeling relationships at multiple scales to improve accuracy of large recommender systems. In: Proc. KDD 2007, pp. 95–104 (2007)
Chang, F., et al.: Bigtable: A distributed storage system for structured data. In: Proc. of OSDI 2006, pp. 205–218 (2006)
Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: Scalable online collaborative filtering. In: Proc. of WWW 2007, pp. 271–280 (2007)
Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: Proc. OSDI 2004, San Francisco, pp. 137–150 (2004)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Amer. Soc. Info. Sci. 41(6), 391–407 (1999)
Ghemawat, S., Gobioff, H., Leung, S.-T.: The Google File System. In: Proc. of SOSP 2003, pp. 29–43 (2003)
Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proc. of CHI 1995, Denver (1995)
Krulwich, B., Burkey, C.: Learning user information interests through extraction of semantically significant phrases. In: Proc. AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA (March 1996)
Kurucz, M., Benczur, A.A., Csalogany, K.: Methods for large scale SVD with missing values. In: Proc. KDD Cup and Workshop (2007)
Lang, K.: NewsWeeder: Learning to filter Netnews. In: Proc. ICML 1995, pp. 331–339 (1995)
Lim, Y.J., Teh, Y.W.: Variational bayesian approach to movie rating prediction. In: Proc. KDD Cup and Workshop (2007)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7, 76–80 (2003)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proc. KDD Cup and Workshop (2007)
Popescul, A., Ungar, L., Pennock, D., Lawrence, S.: Probabilistic models for unified collaborative and content-based recommendation in Sparse-Data Environments. In: Proc. UAI, pp. 437–444 (2001)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of Netnews. In: Proc. the ACM Conference on Computer-Supported Cooperative Work, Chapel Hill, NC (1994)
Salakhutdinov, R., Mnih, A., Hinton, G.E.: Restricted boltzmann machines for collaborative filtering. In: Proc. ICML, pp. 791–798 (2007)
Takacs, G., Pilaszy, I., Nemeth, B., Tikk, D.: On the gravity recommendation system. In: Proc. KDD Cup and Workshop (2007)
Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-posed Problems. John Wiley, New York (1977)
Wu, M.: Collaborative filtering via ensembles of matrix factorizations. In: Proc. KDD Cup and Workshop (2007)
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Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R. (2008). Large-Scale Parallel Collaborative Filtering for the Netflix Prize. In: Fleischer, R., Xu, J. (eds) Algorithmic Aspects in Information and Management. AAIM 2008. Lecture Notes in Computer Science, vol 5034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68880-8_32
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DOI: https://doi.org/10.1007/978-3-540-68880-8_32
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