tutorial

Causal Recommendation: Progresses and Future Directions

Authors:

Xiangnan HeAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3432 - 3435

https://doi.org/10.1145/3539618.3594245

Published: 18 July 2023 Publication History

Abstract

Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (ie correlation) from users' behaviors. However, they still suffer from several issues such as biases and unfairness due to spurious correlations. Considering the causal mechanism behind data can avoid the influences of such spurious correlations. In this light, embracing causal recommender modeling is an exciting and promising direction.

In this tutorial, we aim to introduce the key concepts in causality and provide a systemic review of existing work on causal recommendation. We will introduce existing methods from two different causal frameworks --- the potential outcome (PO) framework and the structural causal model (SCM). We will give examples and discussions regarding how to utilize different causal tools under these two frameworks to model and solve problems in recommendation. Moreover, we will summarize and compare the paradigms of PO-based and SCM-based recommendation. Besides, we identify some open challenges and potential future directions for this area. We hope this tutorial could stimulate more ideas on this topic and facilitate the development of causality-aware recommender systems.

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https://doi.org/10.1109/TAI.2023.3319606
Zhang HWang SLi HZheng CChen XLiu LLuo SWu P(2024)Uncovering the Propensity Identification Problem in Debiased Recommendations2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00056(653-666)Online publication date: 13-May-2024
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Index Terms

Causal Recommendation: Progresses and Future Directions
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

Yang CDai SHou YZhao WXu JSong YZhu HBaeza-Yates RBonchi F(2024)Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and MethodProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671734(3714-3723)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671734
Wang HWang ZNiu YLiu ZLi HLiao YHuang YLiu X(2024)An Accurate and Interpretable Framework for Trustworthy Process MonitoringIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33196065:5(2241-2252)Online publication date: May-2024
https://doi.org/10.1109/TAI.2023.3319606
Zhang HWang SLi HZheng CChen XLiu LLuo SWu P(2024)Uncovering the Propensity Identification Problem in Debiased Recommendations2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00056(653-666)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00056
Cai RLu RChen WHao Z(2024)Counterfactual contextual bandit for recommendation under delayed feedbackNeural Computing and Applications10.1007/s00521-024-09800-036:23(14599-14613)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09800-0
Li HXiao YZheng CWu PCui PKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Propensity mattersProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619239(20182-20194)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619239
Huang SLi HLi QZheng CLiu LEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Pareto Invariant Representation Learning for Multimedia RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612591(6410-6419)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612591
Wang WLin XWang LFeng FWei YChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Equivariant Learning for Out-of-Distribution Cold-start RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612522(903-914)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612522
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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