UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929335021076480 |
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| author | Pan, Lei Soo, Von-Wun |
| author_facet | Pan, Lei Soo, Von-Wun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_07048 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering Pan, Lei Soo, Von-Wun Information Retrieval Machine Learning 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. |
| title | UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2308.07048 |