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2024 | OriginalPaper | Buchkapitel

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

verfasst von : Lei Pan, Von-Wun Soo

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

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Abstract

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.

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Vorheriges Kapitel Conditional Denoising Diffusion for Sequential Recommendation

Nächstes Kapitel Towards Multi-subsession Conversational Recommendation

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Titel: UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering
verfasst von: Lei Pan
Von-Wun Soo
Verlag: Springer Nature Singapore
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-981-9722-64-8

Electronic ISBN: 978-981-9722-62-4

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2262-4_14

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