Entity-driven user intent inference for knowledge graph-based recommendation

It has been proven that a knowledge graph (KG) has the ability to improve the accuracy of recommendations, owing to its capability of storing the auxiliary information of items in a heterogeneous structure. Recently, intent inference methods have been developed to explore the user preference information from a KG and user-item interactions and to help improve the recommendation accuracy. The inferred user intent can also be regarded as a part of the reason why the recommendation model recommends a certain item to a user. It is known that there are two types of information in a KG: entities and relations. However, existing recommendation models infer user intents from only the information contained in the relations in a KG.