research-article

Neuro-Symbolic Interpretable Collaborative Filtering for Attribute-based Recommendation

Authors:

Jianyong WangAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 3229 - 3238

https://doi.org/10.1145/3485447.3512042

Published: 25 April 2022 Publication History

Abstract

Recommender System (RS) is ubiquitous on today’s Internet to provide multifaceted personalized information services. While an enormous success has been made in pushing forward high-accuracy recommendations, the other side of the coin — the recommendation explainability — needs to be better handled for pursuing persuasiveness, especially for the era of deep learning based recommendation. A few research efforts investigate interpretable recommendation from the feature and result levels. Compared with them, model-level explanation, which unfolds the reasoning process of recommendation through transparent models, still remains underexplored and deserves more attention.

In this paper, we propose a model-based explainable recommendation approach, i.e., NS-ICF, which stands for Neuro-Symbolic Interpretable Collaborative Filtering. Thanks to the recent advance on neuro-symbolic computation for automatic rule learning, NS-ICF learns interpretable recommendation rules (consisting of user and item attributes) based on neural networks with two innovations: (1) a three-tower architecture tailored for the user and item sides in the RS domain; (2) fusing the powerful personalized representations of users and items to achieve adaptive rule weights and without sacrificing interpretability. Comprehensive experiments on public datasets demonstrate NS-ICF is comparable to state-of-the-art deep recommendation models and is transparent for its unique neuro-symbolic architecture.

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Cited By

Lin GZhang Y(2025)Fuzzy Neural Logic Reasoning for Robust ClassificationACM Transactions on Knowledge Discovery from Data10.1145/370472819:2(1-29)Online publication date: 14-Feb-2025
https://dl.acm.org/doi/10.1145/3704728
Zhang XShi SLi YMa WSun PZhang M(2024)Feature-Enhanced Neural Collaborative Reasoning for Explainable RecommendationACM Transactions on Information Systems10.1145/369038143:1(1-33)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690381
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
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Neuro-Symbolic Interpretable Collaborative Filtering for Attribute-based Recommendation

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

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Published: 25 April 2022

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National Natural Science Foundation of China

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Lin GZhang Y(2025)Fuzzy Neural Logic Reasoning for Robust ClassificationACM Transactions on Knowledge Discovery from Data10.1145/370472819:2(1-29)Online publication date: 14-Feb-2025
https://dl.acm.org/doi/10.1145/3704728
Zhang XShi SLi YMa WSun PZhang M(2024)Feature-Enhanced Neural Collaborative Reasoning for Explainable RecommendationACM Transactions on Information Systems10.1145/369038143:1(1-33)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690381
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/36528913:2(1-68)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3652891
Chua HDu YSun ZWang ZZhang JOng Y(2024)Unified Denoising Training for RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688109(612-621)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688109
Guo YCai FPan ZShao TChen HZhang X(2024)A counterfactual explanation method based on modified group influence function for recommendationComplex & Intelligent Systems10.1007/s40747-024-01547-410:6(7631-7643)Online publication date: 27-Jul-2024
https://doi.org/10.1007/s40747-024-01547-4
Chen JLiu ZHuang XWu CLiu QJiang GPu YLei YChen XWang XZheng KLian DChen E(2024)When large language models meet personalization: perspectives of challenges and opportunitiesWorld Wide Web10.1007/s11280-024-01276-127:4Online publication date: 28-Jun-2024
https://doi.org/10.1007/s11280-024-01276-1
Gao LZhan HSheng V(2024)Exploring Task-Specific Dimensions in Word Embeddings Through Automatic Rule LearningArtificial Neural Networks and Machine Learning – ICANN 202410.1007/978-3-031-72341-4_14(199-214)Online publication date: 17-Sep-2024
https://doi.org/10.1007/978-3-031-72341-4_14
Yuan HZhao PXian XLiu GLiu YSheng VZhao LElkind E(2023)Sequential recommendation with probabilistic logical reasoningProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/270(2432-2440)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/270
Li XPeng HWang HHuang QXu Z(2023) PCA and Binary -Means Clustering Based Collaborative Filtering Recommendation Journal of Sensors10.1155/2023/27244182023(1-13)Online publication date: 11-Apr-2023
https://doi.org/10.1155/2023/2724418
Carraro T(2023)Overcoming Recommendation Limitations with Neuro-Symbolic IntegrationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608876(1325-1331)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608876
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