The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
Open accessAbstract
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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“Users' implicit feedback can also be influenced by conformity bias; for example, a user might click on an item simply because many other users have done so [66].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [66]
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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)
- Venue
- Proceedings of the Web Conference 2021
- Publisher
- ACM
- Published
- 2021-04-19
- Pages
- 2980-2991
- 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
- hybrid
- doi.org
- 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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