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 be influenced by conformity bias.
“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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Self-citationZheng, Y., Gao, C., Ding, J., Yi, L., Jin, D., Li, Y., & Wang, M. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548428 · Cited by 29 · see citing works
Self-citationPersonalized Latent Structure Learning for Recommendation
Zhang, S., Feng, F., Kuang, K., Zhang, W., Zhao, Z., Yang, H., Chua, T., & Wu, F. Q. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3247563 · Cited by 29 · see citing works
Biases in scholarly recommender systems: impact, prevalence, and mitigation
Färber, M., Coutinho, M., & Yuan, S. (2023) · Scientometrics · doi:10.1007/s11192-023-04636-2 · Cited by 28 · see citing works
Robust Collaborative Filtering to Popularity Distribution Shift
Zhang, A., Ma, W., Zheng, J., Wang, X., & Chua, T. (2023) · ACM Transactions on Information Systems · doi:10.1145/3627159 · Cited by 28 · see citing works
Gupta, P., Sharma, A., Malhotra, P., Vig, L., & Shroff, G. M. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482071 · Cited by 28 · see citing works
Multimodal Graph Causal Embedding for Multimedia-Based Recommendation
Li, S., Xue, F., Liu, K., Guo, D., & Hong, R. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3424268 · Cited by 27 · see citing works
User Consumption Intention Prediction in Meituan
Ping, Y., Gao, C., Liu, T., Du, X., Luo, H., Jin, D., & Li, Y. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467178 · Cited by 27 · see citing works
Self-citationDeconfounded recommendation via causal intervention
Yu, D., Li, Q., Wang, X., & Xu, G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.089 · Cited by 27 · see citing works
Interpolative Distillation for Unifying Biased and Debiased Recommendation
Ding, S., Feng, F., He, X., Jin, J., Wang, W., Liao, Y., & Zhang, Y. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3532002 · Cited by 26 · see citing works
Self-citationUncovering User Interest from Biased and Noised Watch Time in Video Recommendation
Zhao, H., Zhang, L., Xu, J., Cai, G., Dong, Z., & Wen, J.-R. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3608797 · Cited by 26 · see citing works
Exploring False Hard Negative Sample in Cross-Domain Recommendation
Ma, H., Xie, R., Meng, L., Chen, X., Zhang, X., Lin, L., & Zhou, J. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3608791 · Cited by 26 · see citing works
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
Wang, C., Feng, F., Zhang, Y., Wang, Q., Hu, X., & He, X. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3300547 · Cited by 25 · see citing works
Self-citationYan, H., Liao, Y. J., Ma, Z., & Ma, X. (2024) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2024.104689 · Cited by 25 · see citing works
A review of causality-based fairness machine learning
Su, C., Yu, G., Wang, J., Yan, Z., & Cui, L. (2022) · Intelligence & Robotics · doi:10.20517/ir.2022.17 · Cited by 24 · see citing works
An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data
Wu, D., Hu, Y., Liu, K., Li, J., Wang, X., Deng, S., Zheng, N., & Luo, X. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3437370 · Cited by 23 · see citing works
SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation
Li, B., Guo, T., Xingquan Zhu, A., Li, Q., Wang, Y., & Chen, F. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570422 · Cited by 23 · see citing works
Ensuring User-side Fairness in Dynamic Recommender Systems
Yoo, H., Zeng, Z., Kang, J. L., Qiu, R., Zhou, D., Liu, Z., Wang, F., Xu, C., Chan, E., & Tong, H. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645536 · Cited by 23 · see citing works
Recommender Systems Meet Large Language Model Agents: A Survey
Zhu, X., Wang, Y., Gao, H., Xu, W., Wang, C., Liu, Z., Wang, K., Jin, M., Pang, L. X., Weng, Q., et al. (2025) · Foundations and Trends® in Privacy and Security · doi:10.1561/3300000050 · Cited by 21 · see citing works
Dynamic Causal Collaborative Filtering
Xu, S., Tan, J., Fu, Z., Ji, J., Heinecke, S., & Zhang, Y. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557300 · Cited by 21 · see citing works
TopicVAE: Topic-aware Disentanglement Representation Learning for Enhanced Recommendation
Guo, Z., Li, G., Li, J., & Chen, H. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548294 · Cited by 21 · see citing works
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
Ren, W., Wang, L., Liu, K., Guo, R., Peng, L. E., & Fu, Y. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00054 · Cited by 21 · see citing works
Recommendation with Causality enhanced Natural Language Explanations
Zhang, J., Chen, X., Tang, J., Shao, W., Dai, Q., Dong, Z., & Zhang, R. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583260 · Cited by 21 · see citing works
Debiased Recommendation with Noisy Feedback
Li, H., Zheng, C., Wang, W., Wang, H., Feng, F., & Zhou, X. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671915 · Cited by 21 · see citing works
Interact with the Explanations: Causal Debiased Explainable Recommendation System
Liu, X., Yu, T., Xie, K., Wu, J., & Li, S. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635855 · Cited by 20 · see citing works
Position-aware graph neural network for session-based recommendation
Sang, S. B., Yuan, W., Li, W., Yang, Z., Zhang, Z., & Liu, N. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110201 · Cited by 20 · see citing works
Disentangled Representation for Diversified Recommendations
Zhang, X., Wang, H., & Li, H. (2023) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3539597.3570389 · Cited by 20 · see citing works
Alleviating Video-length Effect for Micro-video Recommendation
Quan, Y., Ding, J., Gao, C., Li, N., Yi, L., Jin, D., & Li, Y. (2023) · ACM Transactions on Information Systems · doi:10.1145/3617826 · Cited by 20 · see citing works
Self-citationPopularity-aware sequential recommendation with user desire
Wu, J., Yang, B., Mao, R., & Li, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121429 · Cited by 20 · see citing works
Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets
Wang, K., Liang, Y., Li, X., Li, G., Ghanem, B., Zimmermann, R., Zhou, Z., Yi, H., Zhang, Y., & Wang, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3342184 · Cited by 20 · see citing works
Self-citationLiu, Q., Tian, F., Zheng, Q., & Wang, Q. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01839-0 · Cited by 19 · see citing works
Lee, G., Kim, K.-H., & Shin, K. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688176 · Cited by 19 · see citing works
Negative Sampling in Recommendation: A Survey and Future Directions
Ma, H., Xie, R., Meng, L., Feng, F., Du, X., Sun, X., Kang, Z., & Meng, X. (2026) · ACM Transactions on Information Systems · doi:10.1145/3793855 · Cited by 19 · see citing works
Fu, Z., Xian, Y., Geng, S., de Melo, G., & Zhang, Y. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482461 · Cited by 19 · see citing works
Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation
Bai, Z., Wu, N., Cai, F., Zhu, X., & Xiong, Y. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679611 · Cited by 18 · see citing works
KDCRec: Knowledge Distillation for Counterfactual Recommendation via Uniform Data
Liu, D., Cheng, P., Lin, Z., Luo, J., Dong, Z., He, X., Pan, W., & Ming, Z. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3199585 · Cited by 18 · see citing works
Self-citationCausal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Zhang, S., Jiang, Z., Yao, J., Feng, F., Kuang, K., Zhao, Z., Li, S., Yang, H., Chua, T., & Wu, F. Q. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3290545 · Cited by 18 · see citing works
Aligning Dual Disentangled User Representations from Ratings and Textual Content
Tran, N.-T., & Lauw, H. W. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539474 · Cited by 18 · see citing works
Enhancing Recommendation with Search Data in a Causal Learning Manner
Si, Z., Sun, Z., Zhang, X., Xu, J., Song, Y., Zang, X., & Wen, J.-R. (2023) · ACM Transactions on Information Systems · doi:10.1145/3582425 · Cited by 17 · see citing works
CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation
Ying, Y., Zhuang, F., Zhu, Y., Wang, D., & Zheng, H. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583538 · Cited by 17 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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