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

• URL: http://arxiv.org/abs/2308.07048v1
• Date: Mon, 14 Aug 2023 10:18:24 GMT
• Title: UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering
• Authors: Lei Pan and Von-Wun Soo
• Abstract summary: We propose UIPC-MF, a prototype-based matrix factorization method for explainable collaborative filtering recommendations. 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.
• Score: 2.921387082153523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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