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Recommending items to potentially interested users has been an importantcommercial task that faces two main challenges: accuracy and explainability.While most collaborative filtering models rely on statistical computations on alarge scale of interaction data between users and items and can achieve highperformance, they often lack clear explanatory power. We propose UIPC-MF, aprototype-based matrix factorization method for explainable collaborativefiltering recommendations. In UIPC-MF, both users and items are associated withsets of prototypes, capturing general collaborative attributes. To enhanceexplainability, UIPC-MF learns connection weights that reflect the associativerelations between user and item prototypes for recommendations. UIPC-MFoutperforms other prototype-based baseline methods in terms of Hit Ratio andNormalized Discounted Cumulative Gain on three datasets, while also providingbetter transparency.

UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering