Code and Data Repository for Eliminating Social Popularity Bias in Recommendation: Causal Inference-Based Social Graph Neural Networks

Social recommender models not only exhibit a well-known bias toward popular items but also have a social popularity bias that is often overlooked in existing research. Both biases can lead the model to learn inaccurate user representations, ultimately compromising the diversity and accuracy of recommendations. Using the backdoor adjustment operator and the counterfactual reasoning strategy as key components, a causal inference-based social graph neural network (CISGNN) is proposed.