  Recommending items to potentially interested users has been an important
commercial task that faces two main challenges: accuracy and explainability.
While most collaborative filtering models rely on statistical computations on a
large scale of interaction data between users and items and can achieve high
performance, they often lack clear explanatory power. We propose UIPC-MF, a
prototype-based matrix factorization method for explainable collaborative
filtering recommendations. In UIPC-MF, both users and items are associated with
sets of prototypes, capturing general collaborative attributes. To enhance
explainability, UIPC-MF learns connection weights that reflect the associative
relations between user and item prototypes for recommendations. UIPC-MF
outperforms other prototype-based baseline methods in terms of Hit Ratio and
Normalized Discounted Cumulative Gain on three datasets, while also providing
better transparency.
