research-article

User-Centric Path Reasoning towards Explainable Recommendation

Authors:

Liang-Ying Huang,

Chien-Kun Huang,

Lun-Wei KuAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 879 - 889

https://doi.org/10.1145/3404835.3462847

Published: 11 July 2021 Publication History

Abstract

There has been significant progress in the utilization of heterogeneous knowledge graphs (KG) as auxiliary information in recommendation systems. Reasoning over KG paths sheds light on the user's decision-making process. Previous methods focus on formulating this process as a multi-hop reasoning problem. However, without some form of guidance in the reasoning process, such a huge search space results in poor accuracy and little explanation diversity. In this paper, we propose UCPR, a user-centric path reasoning network that constantly guides the search from the aspect of user demand and enables explainable recommendations. In this network, a multi-view structure leverages not only local sequence reasoning information but also a panoramic view of the user's demand portfolio while inferring subsequent user decision-making steps. Experiments on five real-world benchmarks show UCPR is significantly more accurate than state-of-the-art methods. Besides, we show that the proposed model successfully identifies users' concerns and increases reason-ing diversity to enhance explainability

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Cited By

Möller LPadó S(2024)Explaining Neural News Recommendation with Attributions onto Reading HistoriesACM Transactions on Intelligent Systems and Technology10.1145/3673233Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3673233
Balloccu GBoratto LFenu GMarras MSoccol A(2024)KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691703(1079-1084)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691703
Zhang YWu YHan RSun YZhu YLi XLin WZhuang FAn ZXu YBaeza-Yates RBonchi F(2024)Unified Dual-Intent Translation for Joint Modeling of Search and RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671519(6291-6300)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671519
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Index Terms

User-Centric Path Reasoning towards Explainable Recommendation
1. Computing methodologies
  1. Artificial intelligence
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
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Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

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Published: 11 July 2021

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SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

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Cited By

Möller LPadó S(2024)Explaining Neural News Recommendation with Attributions onto Reading HistoriesACM Transactions on Intelligent Systems and Technology10.1145/3673233Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3673233
Balloccu GBoratto LFenu GMarras MSoccol A(2024)KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691703(1079-1084)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691703
Zhang YWu YHan RSun YZhu YLi XLin WZhuang FAn ZXu YBaeza-Yates RBonchi F(2024)Unified Dual-Intent Translation for Joint Modeling of Search and RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671519(6291-6300)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671519
Lin YLiu YLin FZou LWu PZeng WChen HMiao C(2024)A Survey on Reinforcement Learning for Recommender SystemsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328016135:10(13164-13184)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3280161
Yang ZHe ZWang CLai JTian Z(2024)Collaborative Meta-Path Modeling for Explainable RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.324393911:2(1805-1815)Online publication date: Apr-2024
https://doi.org/10.1109/TCSS.2023.3243939
Alizadeh Noughabi HBehkamal BZarrinkalam FKahani M(2024)Persuasive explanations for path reasoning recommendationsJournal of Intelligent Information Systems10.1007/s10844-024-00896-3Online publication date: 8-Oct-2024
https://doi.org/10.1007/s10844-024-00896-3
Zheng JJing TCao FKang YChen QLi Y(2023)A Multiscale Neighbor-Aware Attention Network for Collaborative FilteringElectronics10.3390/electronics1220437212:20(4372)Online publication date: 22-Oct-2023
https://doi.org/10.3390/electronics12204372
Zhang YSun YZhuang FZhu YAn ZXu Y(2023)Triple Dual Learning for Opinion-based Explainable RecommendationACM Transactions on Information Systems10.1145/363152142:3(1-27)Online publication date: 30-Dec-2023
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Chen HWang BYang KXie L(2023)Persuasive-oriented Explanation Generation and Evaluation of Personalized RecommendationProceedings of the 2023 8th International Conference on Big Data and Computing10.1145/3624288.3624299(74-80)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3624288.3624299
Vijayaraghavan SMohapatra P(2023)Stability of Explainable RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608853(947-954)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608853
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