Abstract
Recommender systems help individuals in a community to find information or items that are most likely to meet their needs. In this paper, we propose a new recommendation model called non-negative matrix factorization for recommender systems based on dynamic bias (NMFRS-DB). As well as the two factor matrices, the proposed method incorporates two bias matrices, which improve the interpretability of the recommendations by expressing differences between observed and estimated ratings. First, the relevant probabilistic distributions are modeled, and then the factor matrices and bias matrices are calculated. Finally, the algorithm is described and explained. To evaluate the proposed method, we conduct experiments on three real-world datasets. The experimental results demonstrate the effectiveness of the NMFRS-DB model.
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Acknowledgments
This work was partially supported by the Great Wall Scholar Program (CIT&TCD20190305), High Innovation Program of Beijing (2015000026833ZK04), and Beijing Urban Governance Research Center.
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Song, W., Li, X. (2019). A Non-Negative Matrix Factorization for Recommender Systems Based on Dynamic Bias. In: Torra, V., Narukawa, Y., Pasi, G., Viviani, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2019. Lecture Notes in Computer Science(), vol 11676. Springer, Cham. https://doi.org/10.1007/978-3-030-26773-5_14
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DOI: https://doi.org/10.1007/978-3-030-26773-5_14
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