Causal Intervention for Sentiment De-biasing in Recommendation

Biases and de-biasing in recommender systems have received increasing attention recently. This study focuses on a newly identified bias, i.e., sentiment bias, which is defined as the divergence in recommendation performance between positive users/items and negative users/items. Existing methods typically employ a regularization strategy to eliminate the bias. However, blindly fitting the data without modifying the training procedure would result in a biased model, sacrificing recommendation performance. In this study, we resolve the sentiment bias with causal reasoning. We develop a causal graph to model the cause-effect relationships in recommender systems, in which the sentiment polarity presented by review text acts as a confounder between user/item representations and observed ratings. The existence of confounders inspires us to go beyond conditional probability and embrace causal inference. To that aim, we use causal intervention in model training to remove the negative effect of sentiment bias. Furthermore, during model inference, we adjust the prediction score to produce personalized recommendations. Extensive experiments on five benchmark datasets validate that the deconfounded training can remove the sentiment bias and the inference adjustment is helpful to improve recommendation accuracy.