Abstract
Matrix Approximation (MA) is a powerful technique in recommendation systems. There are two main problems in the prevalent MA framework. First, the latent factor is out of explanation and hampers the understanding of the reasons behind recommendations. Besides, traditional MA methods produce user/item factors globally, which fails to capture the idiosyncrasies of users/items. In this paper, we propose a model called Boosted Local rank-One Matrix Approximation (BLOMA). The core idea is to locally and sequentially approximate the residual matrix (which represents the unexplained part obtained from the previous stage) by rank-one sub-matrix factorization. The result factors are distinct and explainable by leveraging social networks and item attributes.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61403062, 61433014, 41601025), Science-Technology Foundation for Young Scientist of Sichuan Province (2016JQ0007), Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China (161062) and National key research and development program (2016YFB0502300).
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Gao, C., Yuan, S., Zhang, Z., Yin, H., Shao, J. (2019). BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_72
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DOI: https://doi.org/10.1007/978-3-030-18590-9_72
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