Disentangling User Interest and Conformity for Recommendation with Causal Embedding

By Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, Yong Li. The Web Conference, 2021

413 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/aee144e94c0264fadc57670a1b600069348a1ce8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Fuli Feng
4. Meng Wang
5. Tat-seng Chua
6. Wenjie Wang
7. Jiawei Chen
8. Tianxin Wei

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Fuli Feng
4. Meng Wang
5. Tat-seng Chua
6. Wenjie Wang
7. Jiawei Chen
8. Tianxin Wei

Citations in the corpus

• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System (2020, methodology): “[58] also tries to mitigate popularity bias via causal approaches.”
• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation (2023, methodology): “Moving forward, we plan to explore more efficient methods to activate the recommendation ability of larger models and tune LLMs to handle multiple recommendation tasks simultaneously.”
• Bias and Debias in Recommender System: A Survey and Future Directions (2020, background): “Some recent work further study conformity bias in a more fine-grained manner.”