CaDRec: A Contextualized and Debiased Recommender Model for Enhancing Recommendation Accuracy

This paper proposes CaDRec, a contextualized and debiased recommender model that effectively mitigates the over-smoothing issue in graph convolution networks (GCNs) and tackles the skewed distribution of user-item interactions caused by popularity and user-individual biases.