SCIENTIA SINICA Informationis, Volume 50 , Issue 10 : 1544(2020) https://doi.org/10.1360/SSI-2019-0107

Efficient solution of the SVD recommendation model with implicit feedback

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  • ReceivedApr 20, 2019
  • AcceptedJul 16, 2019
  • PublishedOct 15, 2020


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[1] Shi Y, Larson M, Hanjalic A. Collaborative Filtering beyond the User-Item Matrix. ACM Comput Surv, 2014, 47: 1-45 CrossRef Google Scholar

[2] Linden G, Smith B, York J. Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput, 2003, 7: 76-80 CrossRef Google Scholar

[3] Bell R M, Koren Y. Lessons from the Netflix prize challenge. SIGKDD Explor Newsl, 2007, 9: 75-79 CrossRef Google Scholar

[4] Johnson C C. Logistic matrix factorization for implicit feedback data. In: Proceedings of Advances in Neural Information Processing Systems, 2014. 27. Google Scholar

[5] Zhang S, Wang W, Ford J, et al. Using singular value decomposition approximation for collaborative filtering. In: Proceedings of the 7th IEEE International Conference on E-Commerce Technology (CEC'05). New York: IEEE, 2005. 257--264. Google Scholar

[6] Paterek A. Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop, 2007. 5--8. Google Scholar

[7] Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2008. 426--434. Google Scholar

[8] Guo G B, Zhang J, Yorke-Smith N. TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, 2015. 123--129. Google Scholar

[9] Tang J L, Hu X, Gao H J, et al. Exploiting local and global social context for recommendation. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, 2013. 2712--2718. Google Scholar

[10] Hu G N, Dai X Y, Qiu F Y. Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback. ACM Trans Knowl Discov Data, 2018, 12: 1-30 CrossRef Google Scholar

[11] Tian Y, Qin Y B, Xu D Y, et al. TrustSVD algorithm based on double trust mechanism. J Front Comput Sci Tech, 2015, 9: 1391--1397. Google Scholar

[12] Ruder S. An overview of gradient descent optimization algorithms. 2016,. arXiv Google Scholar

[13] Hu Y F, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. Washington: IEEE Computer Society, 2008. 8: 263--272. Google Scholar

[14] Gates M, Anzt H, Kurzak J, et al. Accelerating collaborative filtering using concepts from high performance computing. In: Proceedings of 2015 IEEE International Conference on Big Data. New York: IEEE, 2015. 667--676. Google Scholar

[15] Wang Z, Liu Y, Chiu S. An efficient parallel collaborative filtering algorithm on multi-GPU platform. J Supercomput, 2016, 72: 2080-2094 CrossRef Google Scholar

[16] Kingma D P, Ba J. Adam: A method for stochastic optimization. 2014,. arXiv Google Scholar

[17] Song E B, Shi Q J, Zhu Y M. Acceleration of block coordinate descent method achieves the $O\left(~{1/{k^2}}~\right)$ rate of convergence for a convex function with block coordinate strong convexity. Sci Sin Math, 2016, 46: 1499--1506. Google Scholar

[18] Xu Y, Yin W. A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion. SIAM J Imag Sci, 2013, 6: 1758-1789 CrossRef Google Scholar

[19] Shi Q, Sun H, Lu S. Inexact Block Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization. IEEE Trans Signal Process, 2017, 65: 5995-6008 CrossRef ADS arXiv Google Scholar

[20] Barnes R J. Matrix differentiation. Springs Journal, 2006: 1-9. Google Scholar

[21] Laue S, Mitterreiter M, Giesen J. Computing higher order derivatives of matrix and tensor expressions. In: Proceedings of Advances in Neural Information Processing Systems, 2018. 2750--2759. Google Scholar

[22] Magnus J R, Neudecker H. Matrix Differential Calculus With Applications in Statistics and Econometrics. Hoboken: John Wiley & Sons, 2019. Google Scholar

[23] Sherman J, Morrison W J. Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix. Ann Math Statist, 1950, 21: 124-127 CrossRef Google Scholar

[24] Hager W W. Updating the Inverse of a Matrix. SIAM Rev, 1989, 31: 221-239 CrossRef Google Scholar

[25] Wang S N, Liu J S, Shroff N. Coded sparse matrix multiplication. 2018,. arXiv Google Scholar

[26] Bulu? A, Gilbert J R. Parallel Sparse Matrix-Matrix Multiplication and Indexing: Implementation and Experiments. SIAM J Sci Comput, 2012, 34: C170-C191 CrossRef Google Scholar

[27] Winlaw M, Hynes M B, Caterini A, et al. Algorithmic acceleration of parallel ALS for collaborative filtering: Speeding up distributed big data recommendation in spark. In: Proceedings of the 21st International Conference on Parallel and Distributed Systems (ICPADS). New York: IEEE, 2015. 682--691. Google Scholar