Aggregating Neighbourhood Information of Items with Knowledge Graph and User Intent Information behind Interaction for Recommendation

In knowledge graph-based recommendation systems, modeling based on item features is a crucial direction. To further enhance the effectiveness of recommendation, we can study common similarities among items based on their shared attributes. The attribute information of items can assist recommendation models in understanding the structure and semantics of items. Existing models face the challenge of not considering user intent when interpreting item attribute information. In this paper, we propose the KGCIN model, which explores user preferences on the knowledge graph. By integrating the structure and semantic information of the knowledge graph and increasing the receptive field factor of entities, the KGCIN model ultimately obtains item embeddings with neighborhood information aggregation results that incorporate user preference understanding. On the other hand, user representations are obtained through information aggregation in the user intent graph. Finally, personalized recommendations for users are made by aggregating representations of user preferences and item information. Experiments in this paper are conducted on sparse dataset Last.FM and dense dataset MOOPer, respectively, validating that the proposed model outperforms other methods based on knowledge graph information fusion or user preference understanding on different datasets. According to experimental results, the model shows improvements in recommendation metrics such as precision and recall.