Implicit Knowledge Graph Collaborative Filtering Model

In the existing recommendation methods based on knowledge graphs, graph neural networks are utilized to capture the correlation between user preferences and knowledge entities to achieve optimal recommendation results. However, certain limitations occur in this kind of relevance modeling methods due to its dependence on the explicit relationship between nodes(users, items or entities). To address these problems, an implicit knowledge graph collaborative filtering model(IKGCF) is proposed. Firstly, the implicit collaborative knowledge graph is constructed to eliminate the interference of explicit relationship on implicit interaction in recommendations and remove the limitation of explicit relationship on semantic relevance in the graph. Then, an enhanced graph neural network module is adopted to perform neighbor aggregation and message propagation to better capture the higher-order relevance on the implicit collaborative knowledge graph. Finally, a layer selection mechanism is employed to obtain the final node embedding vectors and predict and optimize the model. Experiments on three public datasets show that IKGCF achieves better performance. The full code of IKGCF is open-sourced at https://github.com/hfutmars/IKGCF. © 2022 Journal of Pattern Recognition and Artificial Intelligence. All rights reserved.