Abstract
Item recommendation helps people to discover their potentially interested items among large numbers of items. One most common application is to recommend items on implicit feedback datasets (e.g., listening history, watching history or visiting history). In this paper, we assume that the implicit feedback matrix has local property, where the original matrix is not globally low-rank but some sub-matrices are low-rank. In this paper, we propose Local Weighted Matrix Factorization for implicit feedback (LWMF) by employing the kernel function to intensify local property and the weight function to model user preferences. The problem of sparsity can also be relieved by sub-matrix factorization in LWMF, since the density of sub-matrices is much higher than the original matrix. We propose a heuristic method DCGASC to select sub-matrices which approximate the original matrix well. The greedy algorithm has approximation guarantee of factor \(1-\frac{1}{e}\) to get a near-optimal solution. The experimental results on two real datasets show that the recommendation precision and recall of LWMF are both improved more than 30 % comparing with the best case of WMF.
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Notes
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Pair (u, m) means that the user u discovered the item m.
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References
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)
Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop, vol. 2007, pp. 5–8 (2007)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2007)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
Mackey, L.W., Jordan, M.I., Talwalkar, A.: Divide-and-conquer matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1134–1142 (2009)
Zhang, Y., Zhang, M., Liu, Y., et al.: Localized matrix factorization for recommendation based on matrix block diagonal forms. In: Proceedings of the 22nd International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 1511–1520 (2013)
Lee, J., Kim, S., Lebanon, G., et al.: Local low-rank matrix approximation. In: Proceedings of the 30th International Conference on Machine Learning, pp. 82–90 (2013)
Lee, J., Bengio, S., Kim, S., et al.: Local collaborative ranking. In: Proceedings of the 23rd International Conference on World Wide wWeb, pp. 85–96. ACM (2014)
Beutel, A., Ahmed, A., Smola, A.: Additive co-clustering to approximate matrices succinctly. In: Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, pp. 119–129 (2015)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 8th IEEE International Conference on Data Mining: ICDM 2008, pp. 263–272. IEEE (2008)
Pan, R., Zhou, Y., Cao, B., et al.: One-class collaborative filtering. In: 8th IEEE International Conference on Data Mining, pp. 502–511. IEEE (2008)
Pan, R., Scholz, M.: Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 667–676. ACM (2009)
Rendle, S., Freudenthaler, C., Gantner, Z., et al.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pp. 452–461. AUAI Press (2009)
Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: Proceedings of the 7th ACM International Conference on Web Search, Data Mining, pp. 273–282. ACM (2014)
Yang, D., Chen, T., Zhang, W., et al.: Local implicit feedback mining for music recommendation. In: Proceedings of the 6th ACM Conference on Recommender Systems, pp. 91–98. ACM (2012)
Ilievski, I., Roy, S.: Personalized news recommendation based on implicit feedback. In: Proceedings of the 2013 International News Recommender Systems Workshop, Challenge, pp. 10–15. ACM (2013)
Zibriczky, D., Hidasi, B., Petres, Z., et al.: Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback. UMAP Workshops (2012)
Lian, D., Zhao, C., Xie, X., et al.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 831–840. ACM (2014)
Liu, Y., Wei, W., Sun, A., et al.: Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd ACM International Conference on Information, Knowledge Management, pp. 739–748. ACM (2014)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090. ACM (2011)
Chen, S., Moore, J.L., Turnbull, D., et al.: Playlist prediction via metric embedding. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery, Data Mining, pp. 714–722. ACM (2012)
Chapelle, O., Metlzer, D., Zhang, Y., et al.: Expected reciprocal rank for graded relevance. In: Proceedings of the 18th ACM Conference on Information, Knowledge Management, pp. 621–630. ACM (2009)
Clarke, C.L.A., Kolla, M., Cormack, G.V., et al.: Novelty, diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research, Development in Information Retrieval, pp. 659–666. ACM (2008)
Nemhauser, G., Wolsey, L.A., Fisher, M.: An analysis of approximations for maximizing submodular set functions - I. Math. Program. 14(1), 265–294 (1978)
Acknowledgement
This work was supported by the NSFC grants (No. 61472141, 61370101 and 61021004), Shanghai Leading Academic Discipline Project (No. B412), and Shanghai Knowledge Service Platform Project (No. ZF1213).
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Wang, K., Duan, X., Ma, J., Sha, C., Wang, X., Zhou, A. (2016). Local Weighted Matrix Factorization for Implicit Feedback Datasets. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham. https://doi.org/10.1007/978-3-319-32025-0_24
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