Abstract
In recent years, prototypes have gained traction as an interpretability concept in the Computer Vision Domain, and have also been explored in Recommender System algorithms. This paper introduces UIPC-MF, an innovative prototype-based matrix factorization technique aimed at offering explainable collaborative filtering recommendations. Within UIPC-MF, both users and items link with prototype sets that encapsulate general collaborative features. UIPC-MF uniquely learns connection weights, highlighting the relationship between user and item prototypes, offering a fresh method for determining the final predicted score beyond the conventional dot product. Comparative results show that UIPC-MF surpasses other prototype-based benchmarks in Hit Ratio and Normalized Discounted Cumulative Gain across three datasets, while enhancing transparency.
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Pan, L., Soo, VW. (2024). UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_14
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DOI: https://doi.org/10.1007/978-981-97-2262-4_14
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