Enhancing Explainable Rating Prediction through Annotated Macro Concepts

Generating recommendation reasons for recommendation results is a long-standing problem because it is challenging to explain the underlying reasons for recommending an item based on user and item IDs. Existing models usually learn semantic embeddings for each user and item, and generate the reasons according to the embeddings of the user-item pair. However, user and item IDs do not carry inherent semantic meaning, thus the limited number of reviews cannot model users' preferences and item characteristics effectively, negatively affecting the model generalization for unseen user-item pairs. To tackle the problem, we propose the Concept Enhanced Explainable Recommendation framework (CEER), which utilizes macro concepts as the intermediary to bridge the gap between the user/item embeddings and the recommendation reasons. Specifically, we maximize the information bottleneck to extract macro concepts from user-item reviews. Then, for recommended user-item pairs, we jointly train the concept embeddings with the user and item embeddings, and generate the explanation according to the concepts. Extensive experiments on three datasets verify the superiority of our CEER model.