Explainable Recommendaion Using Review Text and a Knowledge Graph

Recommender systems using a knowledge graph can comprehensively organize users and items and their attributes and thereby improve recommendation performance. In addition, the relationship between users and items can be easily interpreted on the basis of entities and relations, thus giving explanations to recommendations. The algorithms and knowledge graphs used for generating explanations have not utilized review text. We have developed a recommendation method for predicting interactions between users and items using a knowledge graph and review text. The underlying user-item relationships are reflected and explanations are generated by predicting user-item interactions from the paths between a user and an item. The modeling is done using a recurrent neural network or a factorization machine. Items' aspects that interest users are extracted from review text and leveraged using an attention-like mechanism. Since the path between a user and an item can be easily interpreted, and the important aspects between a user and an item can be interpreted by observing the attention weight, the proposed model can generate a reasonable recommendation explanation. Testing using a real-world dataset demonstrated that the proposed model can explain the recommendations.