Mitigating hidden confounding effects for causal recommendation

X Zhu, Y Zhang, X Yang, D Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Recommender systems suffer from confounding biases when there exist confounders
affecting both item features and user feedback (eg, like or not). Existing causal
recommendation methods typically assume confounders are fully observed and measured,
forgoing the possible existence of hidden confounders in real applications. For instance,
product quality is a confounder since it affects both item prices and user ratings, but is
hidden for the third-party e-commerce platform due to the difficulty of large-scale quality …

[CITATION][C] Mitigating hidden confounding effects for causal recommendation. arXiv

X Zhu, Y Zhang, F Feng, X Yang, D Wang, X He - arXiv preprint arXiv:2205.07499, 2022
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