A Recommendation Strategy Integrating Higher-Order Feature Interactions With Knowledge Graphs

Knowledge graphs (KG) are efficient auxiliary information in recommender systems. However, in knowledge graph feature learning, the learning is mainly for triples rather than recommendations, which may bring difficulty in large improvement for recommendation performance. One of the problems in the existing methods is that they cannot uncover the deep interaction information of users in a simple way, and this motivates effective learning the potential embedded information through the knowledge graph. The Graph Convolutional Network (GCN) can be useful for learning information about graph structured data. This paper proposes a method that fuses higher order feature interactions and knowledge graphs and uses them for recommendation. For users, they uses gated recurrent units (GRU) to focus on their preferences so that the ability of convolutional neural networks in processing user preference features is enhanced; for items, the cross-learning module is used to learn higher order features between items and entities; for users and entities, KG and user-item interaction information are combined followed by feature extraction of graph structured data by Light GCN, allowing the model to learn potential user-entity associations in the graph structured data. These procedures enable the model to learn potential user-entity associations in the graph data. Extensive experiments on two real datasets show that the proposed model performs better than the state-of-the-art model.