research-article

Off-policy Learning over Heterogeneous Information for Recommendation

Authors:

Xiangmeng Wang,

Guandong XuAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2348 - 2359

https://doi.org/10.1145/3485447.3512072

Published: 25 April 2022 Publication History

Abstract

Reinforcement learning has recently become an active topic in recommender system research, where the logged data that records interactions between items and users feedback is used to discover the policy. Much off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has been a popular research topic in reinforcement learning. However, the log entries are biased in that the logs over-represent actions favored by the recommender system, as the user feedback contains only partial information limited to the particular items exposed to the user. As a result, the policy learned from such off-line logged data tends to be biased from the true behaviour policy.

In this paper, we are the first to propose a novel off-policy learning augmented by meta-paths for the recommendation. We argue that the Heterogeneous information network (HIN), which provides rich contextual information of items and user aspects, could scale the logged data contribution for unbiased target policy learning. Towards this end, we develop a new HIN augmented target policy model (HINpolicy), which explicitly leverages contextual information to scale the generated reward for target policy. In addition, being equipped with the HINpolicy model, our solution adaptively receives HIN-augmented corrections for counterfactual risk minimization, and ultimately yields an effective policy to maximize the long run rewards for the recommendation. Finally, we extensively evaluate our method through a series of simulations and large-scale real-world datasets, obtaining favorable results compared with state-of-the-art methods.

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Cited By

Wang XLi QYu DLi QXu G(2024)Counterfactual Explanation for Fairness in RecommendationACM Transactions on Information Systems10.1145/364367042:4(1-30)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3643670
Wang XLi QYu DLi QXu G(2024)Reinforced Path Reasoning for Counterfactual Explainable RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335407736:7(3443-3459)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3354077
Wang XLi QYu DHuang WLi QXu G(2024)Neural Causal Graph collaborative filteringInformation Sciences: an International Journal10.1016/j.ins.2024.120872677:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120872
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Index Terms

Off-policy Learning over Heterogeneous Information for Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications

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

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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

Wang XLi QYu DLi QXu G(2024)Counterfactual Explanation for Fairness in RecommendationACM Transactions on Information Systems10.1145/364367042:4(1-30)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3643670
Wang XLi QYu DLi QXu G(2024)Reinforced Path Reasoning for Counterfactual Explainable RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335407736:7(3443-3459)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3354077
Wang XLi QYu DHuang WLi QXu G(2024)Neural Causal Graph collaborative filteringInformation Sciences: an International Journal10.1016/j.ins.2024.120872677:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120872
Shi XWang ZCai YWu XYang FLiao GWang YWang XWang DChen HDuh WHuang HKato MMothe JPoblete B(2023)MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel FeedProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592018(2159-2163)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592018
Li QWang XWang ZXu G(2023)Be Causal: De-Biasing Social Network Confounding in RecommendationACM Transactions on Knowledge Discovery from Data10.1145/353372517:1(1-23)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3533725
Wang XLi QYu DLi QXu G(undefined)Constrained Off-policy Learning over Heterogeneous Information for Fairness-aware RecommendationACM Transactions on Recommender Systems10.1145/3629172
https://dl.acm.org/doi/10.1145/3629172

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