Alleviating Dual Biases in Recommendation (Student Abstract)

AAAI 2025 pp. 29421-29422

doi:10.1609/AAAI.V39I28.35273 /aaai/2025/lu2025aaai-alleviating/

Abstract

Causal Inference (CI) plays a crucial role in building unbiased recommender systems. However, most current CI-based debiasing methods only pay attention on either popularity bias or conformity bias. This paper presents a Disentangled Counterfactual Reasoning framework to alleviate dual biases in recommendation, so called DCR. Concretely, we consider the impact of both item popularity and user conformity during training, and separate their indirect effects by disentangling user and item embeddings into biased and unbiased components. In the inference stage, we perform counterfactual reasoning to simultaneously mitigate the indirect and direct effects of bias factors. Experimental results demonstrate the effectiveness of our DCR.

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Cite

Text

Lu et al. "Alleviating Dual Biases in Recommendation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35273

Markdown

[Lu et al. "Alleviating Dual Biases in Recommendation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/lu2025aaai-alleviating/) doi:10.1609/AAAI.V39I28.35273

BibTeX

@inproceedings{lu2025aaai-alleviating,
  title     = {{Alleviating Dual Biases in Recommendation (Student Abstract)}},
  author    = {Lu, Sijin and Luo, Fangyuan and Wu, Jun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29421-29422},
  doi       = {10.1609/AAAI.V39I28.35273},
  url       = {https://mlanthology.org/aaai/2025/lu2025aaai-alleviating/}
}