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Explainable reasoning over knowledge graphs for recommendation

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Tat-Seng ChuaAuthors Info & Claims

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

Article No.: 653, Pages 5329 - 5336

https://doi.org/10.1609/aaai.v33i01.33015329

Published: 27 January 2019 Publication History

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Abstract

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path.

In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.

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Cited By

Fu Z(2024)A Knowledge Representation Learning Model Integrating Multiple InformationProceedings of the 2024 International Conference on Image Processing, Intelligent Control and Computer Engineering10.1145/3691016.3691065(297-301)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3691016.3691065
Zhou QYin PYan XLi CJiang GCheng J(2024)Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level BatchingProceedings of the ACM on Management of Data10.1145/36771292:4(1-29)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677129
Eytan LBogina VBen-Gal IKoenigstein N(2024)KPAR: Knowledge-aware Path-based Attentive Recommender with InterpretabilityACM Transactions on Recommender Systems10.1145/3673243Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3673243
Show More Cited By

Index Terms

Explainable reasoning over knowledge graphs for recommendation
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms

Index terms have been assigned to the content through auto-classification.

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AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

January 2019

10088 pages

ISBN:978-1-57735-809-1

Copyright © 2019 Association for the Advancement of Artificial Intelligence.

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Published: 27 January 2019

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Cited By

Fu Z(2024)A Knowledge Representation Learning Model Integrating Multiple InformationProceedings of the 2024 International Conference on Image Processing, Intelligent Control and Computer Engineering10.1145/3691016.3691065(297-301)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3691016.3691065
Zhou QYin PYan XLi CJiang GCheng J(2024)Atom: An Efficient Query Serving System for Embedding-based Knowledge Graph Reasoning with Operator-level BatchingProceedings of the ACM on Management of Data10.1145/36771292:4(1-29)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677129
Eytan LBogina VBen-Gal IKoenigstein N(2024)KPAR: Knowledge-aware Path-based Attentive Recommender with InterpretabilityACM Transactions on Recommender Systems10.1145/3673243Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3673243
Meng SZhou JChen XLiu YLu FHuang X(2024)Structure-Information-Based Reasoning over the Knowledge Graph: A Survey of Methods and ApplicationsACM Transactions on Knowledge Discovery from Data10.1145/367114818:8(1-42)Online publication date: 16-Aug-2024
https://dl.acm.org/doi/10.1145/3671148
Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Shashaani S(2024)Explainability in Music Recommender SystemProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688028(1395-1401)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688028
Anelli VFerrara AMusto CNarducci FRagone AZanker M(2024)Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)Proceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687114(1245-1249)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687114
Zhang JTang JChen XYu WHu LJiang PLi HBaeza-Yates RBonchi F(2024)Natural Language Explainable Recommendation with Robustness EnhancementProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671781(4203-4212)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671781
Zhang LWu NSerra ESpezzano F(2024)HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph RecommendationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679701(3186-3196)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679701
Wang BLiu JKarimnazarov JThompson N(2024)Task Supportive and Personalized Human-Large Language Model Interaction: A User StudyProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638344(370-375)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638344
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