Code and Data Repository for Eliminating Social Popularity Bias in Recommendation: Causal Inference-Based Social Graph Neural Networks
针对推荐系统中被忽视的社会流行度偏差，提出基于因果推断的社会图神经网络（CISGNN），利用后门调整和反事实推理来提升推荐的多样性和准确性。
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.