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

Lei Pan; Von Wun Soo

doi:10.1007/978-981-97-2262-4_14

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

Lei Pan^*, Von Wun Soo

^*Corresponding author for this work

Department of Artificial Intelligence

National Tsing Hua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings
Editors	De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	170-181
Number of pages	12
ISBN (Print)	9789819722648
DOIs	https://doi.org/10.1007/978-981-97-2262-4_14
State	Published - 2024
Event	28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024 - Taipei, Taiwan Duration: 07 05 2024 → 10 05 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14649 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024
Country/Territory	Taiwan
City	Taipei
Period	07/05/24 → 10/05/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Keywords

collaborative filtering
explainable recommender system
prototype

Access to Document

10.1007/978-981-97-2262-4_14

Cite this

Pan, L., & Soo, V. W. (2024). UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering. In D.-N. Yang, X. Xie, V. S. Tseng, J. Pei, J.-W. Huang, & J. C.-W. Lin (Eds.), Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings (pp. 170-181). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14649 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-2262-4_14

Pan, Lei ; Soo, Von Wun. / UIPC-MF : User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering. Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings. editor / De-Nian Yang ; Xing Xie ; Vincent S. Tseng ; Jian Pei ; Jen-Wei Huang ; Jerry Chun-Wei Lin. Springer Science and Business Media Deutschland GmbH, 2024. pp. 170-181 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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Pan, L & Soo, VW 2024, UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering. in D-N Yang, X Xie, VS Tseng, J Pei, J-W Huang & JC-W Lin (eds), Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14649 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 170-181, 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, 07/05/24. https://doi.org/10.1007/978-981-97-2262-4_14

UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering. / Pan, Lei; Soo, Von Wun.
Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings. ed. / De-Nian Yang; Xing Xie; Vincent S. Tseng; Jian Pei; Jen-Wei Huang; Jerry Chun-Wei Lin. Springer Science and Business Media Deutschland GmbH, 2024. p. 170-181 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14649 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Pan L, Soo VW. UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering. In Yang DN, Xie X, Tseng VS, Pei J, Huang JW, Lin JCW, editors, Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 170-181. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-97-2262-4_14