Abstract
It is today accepted that matrix factorization models allow a high quality of rating prediction in recommender systems. However, a major drawback of matrix factorization is its static nature that results in a progressive declining of the accuracy of the predictions after each factorization. This is due to the fact that the new obtained ratings are not taken into account until a new factorization is computed, which can not be done very often because of the high cost of matrix factorization. In this paper, aiming at improving the accuracy of recommender systems, we propose a cluster-based matrix factorization technique that enables online integration of new ratings. Thus, we significantly enhance the obtained predictions between two matrix factorizations. We use finer-grained user biases by clustering similar items into groups, and allocating in these groups a bias to each user. The experiments we did on large datasets demonstrated the efficiency of our approach.
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References
Adomavicius, G., and J. Zhang. 2012. Impact of data characteristics on recommender systems performance. ACM Transactions on Management Information Systems 3(1): 3:1–3:17.
Agarwal, D, B.-C. Chen, and P. Elango. 2010. Fast online learning through offline initialization for time-sensitive recommendation. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’10, 703–712. New York: ACM.
Amatriain, X., and J. Basilico. 2012. Netflix recommendations: Beyond the 5 stars, 2012. The Netflix Tech Blog.
Bell, R., Y. Koren, and C. Volinsky. 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, KDD ’07, 95–104. New York: ACM.
Bell, R.M., J. Bennett, Y. Koren, and C. Volinsky. 2009. The million dollar programming prize. IEEE Spectrum 46: 28–33.
Bennett, J., S. Lanning, and N. Netflix. 2007. The netflix prize. In In KDD cup and workshop in conjunction with KDD.
Bickson, D. 2011. Large scale matrix factorization—yahoo! kdd cup, 2011. Large Scale Machine Learning and Other Animals.
Cao, B., D. Shen, J.-T. Sun, X. Wang, Q. Yang, and Z. Chen. 2007. Detect and track latent factors with online nonnegative matrix factorization. In Proceedings of the 20th international joint conference on artificial intelligence, 2689–2694. San Francisco: Morgan Kaufmann Publishers Inc.
Chakraborty, P. 2009. A scalable collaborative filtering based recommender system using incremental clustering. In Advance Computing Conference, IACC 2009. IEEE International, 1526–1529.
Dias, M.B., D. Locher, M. Li, W. El-Deredy, and P.J. Lisboa. 2008. The value of personalised recommender systems to e-business: a case study. In Proceedings of the 2008 ACM conference on recommender systems, RecSys ’08, 291–294. New York: ACM.
Dror, G., N. Koenigstein, Y. Koren, and M. Weimer. 2011. The yahoo! music dataset and kdd-cup’11. In Proceedings of KDDCup 2011.
Fleder, D.M., and K. Hosanagar. 2007. Recommender systems and their impact on sales diversity. In Proceedings of the 8th ACM conference on electronic commerce, EC ’07, 192–199. New York: ACM.
Gemulla, R., E. Nijkamp, P.J. Haas, and Y. Sismanis. 2011. Large-scale matrix factorization with distributed stochastic gradient descent. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’11, 69–77. New York: ACM.
Herlocker, J.L., J.A. Konstan, L.G. Terveen, and J.T. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions Informatics Systems 22: 5–53.
Jambor, T., J. Wang, and N. Lathia. 2012. Using control theory for stable and efficient recommender systems. In Proceedings of the 21st international conference on World Wide Web, WWW ’12, 11–20. New York: ACM.
Jannach, D., and K. Hegelich. 2009. A case study on the effectiveness of recommendations in the mobile internet. In RecSys, ed. L.D. Bergman, A. Tuzhilin, R.D. Burke, A. Felfernig, and L. Schmidt-Thieme, 205–208. ACM.
Koenigstein, N., G. Dror, and Y. Koren. 2011. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In Proceedings of the 5th ACM conference on recommender systems, RecSys ’11, 165–172. New York: ACM.
Kogan, J. 2007. Introduction to clustering large and high-dimensional data. New York: Cambridge University Press.
Kogan, J., C. Nicholas, and M. Teboulle. 2006. Grouping multidimensional data: recent advances in clustering. New York: Springer.
Koren, Y. 2007. How useful is a lower rmse? Netflix Prize Forum.
Koren, Y. 2009. The bellkor solution to the netflix grand prize.
Koren, Y. 2010. Collaborative filtering with temporal dynamics. Communications ACM 53(4): 89–97.
Koren, Y., R. Bell, and C. Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42: 30–37.
Linden, G., B. Smith, and J. York. 2003. Industry report: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Distributed Systems Online 4(1).
Paterek, A. 2007. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD cup workshop at SIGKDD’07, 13th ACM international conference on knowledge discovery and data mining 39–42.
Rendle, S., and L. Schmidt-Thieme. 2008. Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In RecSys, ed. P. Pu, D.G. Bridge, B. Mobasher, and F. Ricci, 251–258. ACM.
Sarwar, B., G. Karypis, J. Konstan, and J. Riedl. 2002. Incremental singular value decomposition algorithms for highly scalable recommender systems. In Proceedings of the 5th international conference in computers and information technology.
Schafer, J.B., J. Konstan, and J. Riedi. 1999. Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on electronic commerce, EC ’99, 158–166. New York: ACM.
Su, X., and T.M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009: 4:2–4:2.
Sun, Y., G. Liu, and K. Xu. 2010. A k-means-based projected clustering algorithm. In Proceedings of the 2010 third international joint conference on computational science and optimization—volume 01, CSO ’10, 466–470. Washington: IEEE Computer Society.
Takács, G., I. Pilászy, B. Németh, and D. Tikk. 2008. Investigation of various matrix factorization methods for large recommender systems. In Proceedings of the 2nd KDD workshop on large-scale recommender systems and the netflix prize competition, NETFLIX ’08, 6:1–6:8. New York: ACM.
Takács, G., I. Pilászy, B. Németh, and D. Tikk. 2009. Scalable collaborative filtering approaches for large recommender systems. Journal Machinery Learning Research 10: 623–656.
TPC-Council. 2010. Tpc benchmark c, rev 5.11. Technical report, Transaction Processing Performance Council.
Ziegler, C.-N., G. Lausen, and J.A. Konstan. 2008. On exploiting classification taxonomies in recommender systems. AI Communications 21(2–3): 97–125.
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Gueye, M., Abdessalem, T., Naacke, H. (2016). Dynamic Recommender System: Using Cluster-Based Biases to Improve the Accuracy of the Predictions. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 615. Springer, Cham. https://doi.org/10.1007/978-3-319-23751-0_5
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