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Cited by 369Recommender Systems and TechniquesInformation Retrieval and Search BehaviorAdvanced Bandit Algorithms Research

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Disentangling User Interest and Conformity for Recommendation with Causal Embedding

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Zheng, Yu, Gao, Chen, Li, Xiang, He, Xiangnan, Li, Yong, Jin, Depeng·2021·Proceedings of the Web Conference 2021
DOI 10.1145/3442381.3449788
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Abstract

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users’ conformity towards popular items, which entangles users’ real interest. Existing methods tracks this problem as eliminating popularity bias, e.g., by re-weighting training samples or leveraging a small fraction of unbiased data. However, the variety of user conformity is ignored by these approaches, and different causes of an interaction are bundled together as unified representations, hence robustness and interpretability are not guaranteed when underlying causes are changing. In this paper, we present DICE, a general framework that learns representations where interest and conformity are structurally disentangled, and various backbone recommendation models could be smoothly integrated. We assign users and items with separate embeddings for interest and conformity, and make each embedding capture only one cause by training with cause-specific data which is obtained according to the colliding effect of causal inference. Our proposed methodology outperforms state-of-the-art baselines with remarkable improvements on two real-world datasets on top of various backbone models. We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.

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Metadata

Authors
Zheng, Y., Gao, C., Li, X., He, X., Li, Y., & Jin, D.
Affiliations
  • Beijing National Research Center for Information Science and Technology (CN)
  • University of Hong Kong (HK)
  • University of Science and Technology of China (CN)
ORCID
Venue
Proceedings of the Web Conference 2021
Publisher
ACM
Published
2021-04-19
Pages
2980-2991
DOI
10.1145/3442381.3449788
OpenAlex
W3156622960
Cited by
356
References
34
Citations by year
2026: 32 · 2025: 78 · 2024: 94 · 2023: 92 · 2022: 51 · 2021: 9
FWCI
51.00
Type
conference paper
Language
en
Open access
Licence
Locations
  • International World Wide Web Conference (WWW) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (U19A2079, 61971267, 61972223, 61861136003, U1936217, 61941117)
  • National Key Research and Development Program of China (2020AAA0106000)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Keywords
recommender systems, popularity bias, disentangled representations, causal inference, interpretability, robustness, user interest modeling

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