research-article

Causal Inference in Recommender Systems: A Survey and Future Directions

Authors:

Xiangnan He, and

Yong LiAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 4

Article No.: 88, Pages 1 - 32

https://doi.org/10.1145/3639048

Published: 09 February 2024 Publication History

Abstract

Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality, not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.

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Jian MBai YFu XGuo JShi GWu L(2024)Counterfactual Graph Convolutional Learning for Personalized RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365563215:4(1-20)Online publication date: 18-Jun-2024
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Liu ZLiu SZhang ZCai QZhao XZhao KHu LJiang PGai KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657829(1872-1882)Online publication date: 10-Jul-2024
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Li NBan XLing CGao CHu LJiang PGai KLi YLiao QHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Modeling User Fatigue for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657802(996-1005)Online publication date: 10-Jul-2024
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Index Terms

Causal Inference in Recommender Systems: A Survey and Future Directions
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 4

July 2024

751 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3613639

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 09 February 2024

Online AM: 02 January 2024

Accepted: 08 November 2023

Revised: 27 September 2023

Received: 25 August 2022

Published in TOIS Volume 42, Issue 4

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Recommender systems; causal inference; information retrieval

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National Key Research and Development Program of China
National Natural Science Foundation of China
Guoqiang Institute, Tsinghua University

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Cited By

Jian MBai YFu XGuo JShi GWu L(2024)Counterfactual Graph Convolutional Learning for Personalized RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365563215:4(1-20)Online publication date: 18-Jun-2024
https://dl.acm.org/doi/10.1145/3655632
Liu ZLiu SZhang ZCai QZhao XZhao KHu LJiang PGai KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657829(1872-1882)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657829
Li NBan XLing CGao CHu LJiang PGai KLi YLiao QHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Modeling User Fatigue for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657802(996-1005)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657802
Zan XSemenov AWang CXian XGeremew W(2024)Causality-aware social recommender system with network homophily informed multi-treatment confoundersInformation Sciences10.1016/j.ins.2024.120729676(120729)Online publication date: Aug-2024
https://doi.org/10.1016/j.ins.2024.120729
Latrech JKodia ZBen Azzouna N(2024)Twit-CoFiD: a hybrid recommender system based on tweet sentiment analysisSocial Network Analysis and Mining10.1007/s13278-024-01288-914:1Online publication date: 27-Jun-2024
https://doi.org/10.1007/s13278-024-01288-9
Ma XLi M(2023)Exploration and Reflection on Precise Recommendation in Complex Information Environments: Boundaries, Challenges, and Future Prospects2023 9th International Conference on Systems and Informatics (ICSAI)10.1109/ICSAI61474.2023.10423324(1-6)Online publication date: 16-Dec-2023
https://doi.org/10.1109/ICSAI61474.2023.10423324
Wang JLi HZhang CLiang DYu EOu WWang W(2023)CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00174(1355-1360)Online publication date: 1-Dec-2023
https://doi.org/10.1109/ICDM58522.2023.00174

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