437 citations · 1.2k across the 41 of their papers we have counts for
4 papers · 1 filter
Mitigating Spurious Correlations for Self-supervised Recommendation
Xinyu Lin, Yiyan Xu, Wenjie Wang +2
Recent years have witnessed the great success of self-supervised learning (SSL) in recommendation systems. However, SSL recommender models are likely to suffer from spurious correl…
Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation
Chenxu Wang, Fuli Feng, Yang Zhang +3
Historical interactions are the default choice for recommender model training, which typically exhibit high sparsity, i.e., most user-item pairs are unobserved missing data. A stan…
Mitigating Hidden Confounding Effects for Causal Recommendation
Xinyuan Zhu, Yang Zhang, Fuli Feng +3
Recommender systems suffer from confounding biases when there exist confounders affecting both item features and user feedback (e.g., like or not). Existing causal recommendation m…
Addressing Confounding Feature Issue for Causal Recommendation
Xiangnan He, Yang Zhang, Fuli Feng +4
In recommender system, some feature directly affects whether an interaction would happen, making the happened interactions not necessarily indicate user preference. For instance, s…