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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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
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
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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

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Guo YCai FPan ZShao TChen HZhang X(2024)A counterfactual explanation method based on modified group influence function for recommendationComplex & Intelligent Systems10.1007/s40747-024-01547-4Online 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
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
Yu LLi MZhang YLi LZhou JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)FINRule: Feature Interactive Neural Rule LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614884(3020-3029)Online publication date: 21-Oct-2023
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Wang ZGao MLi WYu JGuo LYin HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Efficient Bi-Level Optimization for Recommendation DenoisingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599324(2502-2511)Online publication date: 6-Aug-2023
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Ji JLi ZXu SXiong MTan JGe YWang HZhang YChua TLauw HSi LTerzi ETsaparas P(2023)Counterfactual Collaborative ReasoningProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570464(249-257)Online publication date: 27-Feb-2023
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Ahmed KNadeem MZheng ZLi DUllah IAssam MGhadi YMohamed H(2023)Breaking down linguistic complexities: A structured approach to aspect-based sentiment analysisJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10165135:8(101651)Online publication date: Sep-2023
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Wu HHuang CDeng S(2023)Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph NetworkInformation Fusion10.1016/j.inffus.2022.12.00492:C(289-299)Online publication date: 1-Apr-2023
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