Abstract
Matrix factorization has high computation complexity. It is unrealistic to directly adopt such techniques to online recommendation where users, items, and ratings grow constantly. Therefore, implementing an online version of recommendation based on incremental matrix factorization is a significant task. Though some results have been achieved in this realm, there is plenty of room to improve. This paper focuses on designing and implementing algorithms to perform online collaborative filtering recommendation based on incremental matrix factorization techniques. Specifically, for the new-user and new-item problems, Moore-Penrose pseudoinverse is used to perform incremental matrix factorization; and for the new-rating problem, iterative stochastic gradient descentlearning procedure is directly applied to get the updates. The solutions seem simple but efficient: extensive experiments show that they outperform the benchmark and the state-of-the-art in terms of incremental properties.
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References
Bell, R.M., Koren, Y.: Improved neighborhood-based collaborative filtering. In: Proceedings of KDD-Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 7–14 (2007)
Berry, M.W., Dumais, S.T., OBrien, G.W.: Using linear algebra for intelligent information retrieval. Using Linear Algebra for Intelligent Information Retrieval 37(4), 573–595 (1995)
Dumais, S.T., Furnas, G.W., Landauer, T.K.: Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990)
Ge, Y., Xiong, H., Tuzhilin, A., Liu, Q.: Cost-aware collaborative filtering for travel tour recommendations. ACM Transactions on Information Systems 32(1) (2014)
Golub, G.H., Loan, C.F.V.: Matrix Computations, 4th edn. Johns Hopkins University Press (2012)
Ito, K., Jin, B.: Inverse Problems: Tikhonov Theory and Algorithms. World Scientific Publishing Company (2014)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 135–142 (2010)
Koren, Y.: Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 426–434 (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. Communications of the ACM 53(4), 89–97 (2010)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Luo, X., Xia, Y., Zhu, Q.: Incremental collaborative filtering recommender based on regularized matrix factorization. Knowledge-Based Systems 27(3), 271–280 (2012)
Pronk, V., Verhaegh, W., Proidl, A., Tiemann, M.: Incorporating user control into recommender systems based on naive bayesian classification. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 73–80 (2007)
Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 251–258 (2008)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Proceedings of the Fifth International Conference on Computer and Information Science, pp. 27–32 (2002)
Stoer, J., Bulirsch, R.: Introduction to Numerical Analysis, 3rd edn. Springer (2002)
Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter Estimation. SIAM: Society for Industrial and Applied Mathematics (2004)
Wang, Y.X., Zhang, Y.J.: Nonnegative matrix factorization: A comprehensive review. IEEE Transaction on Knowledge and Data Engineering 25(6), 1336–1353 (2013)
Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 221–229 (2013)
Zhang, Y., Koren, J.: Efficient bayesian hierarchical user modeling for recommendation systems. In: Proceedings of SIGIR 2007, pp. 47–54 (2007)
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Zhang, F., Gong, T., Lee, V.E., Zhao, G., Qu, G. (2015). Simple is Beautiful: An Online Collaborative Filtering Recommendation Solution with Higher Accuracy. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_41
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DOI: https://doi.org/10.1007/978-3-319-25255-1_41
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