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

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.