Rating Prediction Model Based on Causal Inference Debiasing Method in Recommendation

The rating prediction task plays an important role in the recommendation model. Most existing methods predict ratings by extracting user and items characteristics from historical review data. However, the recommended strategies in historical review data are often based on partial observational data, which having the problems of unbalanced distribution, lack of robustness, and inability to obtain unbiased prediction results. Therefore, a novel rating prediction model based on causal inference debiasing (CID) method is proposed. The model can mitigate the negative effects of context bias and improve the robustness by studying the causal relationship between review information and user ratings. The proposed CID rating prediction model is plug-and-play and is not limited to one baseline prediction method. The proposed method is tested on four open datasets. The results show that the proposed method is feasible. Compared with the most advanced models, the prediction accuracy of the CID rating prediction model has been further improved. The experimental results show the debiasing effectiveness of the CID rating prediction model.