Feature interactive graph neural network for KG-based recommendation

Graph neural network (GNN) is considered as the state-of-art method for KG-based recommendation. However, the existing GNN-based recommendation methods incorporating KG information fail to fully consider interacyions between nodes in the process of message passing and aggregating, which will affect the performance improvement of recommendation. To resolve the above limitation, we propose a Feature Interactive Graph Neural Network for KG-based Recommendation (FIKGRec) to explicitly model sophisticated feature interactions from the complex structure of heterogeneous knowledge graph. The overall framework consists of three components: (1) For items, we construct item-KGs where the nodes (entities) denote items and items' features, and edges represent the relations between entities. Modeling feature interaction can be thus transformed into modeling node (entity) interaction on the knowledge graph. Specifically, we integrate the collaborative signals into the process of KG signals propagation to capture more precise user preferences and then employ the feature entity interaction layer to incorporate the interaction information between entities in the process of neighbor aggregation of entities in item-KG. (2) For users, a preference-aware attention mechanism is designed to obtain the user's fine-grained preference for items that have been interacted. (3) The final representations of users and items are fed to deep neural network (DNN) to model complex correlations between them. Extensive experiments on three real-world datasets demonstrate the better performance of our FIKGRec framework compared to state-of-the-arts methods.

Automated summary

This paper introduces a Feature Interactive Graph Neural Network for KG-based Recommendation (FIKGRec) to improve the performance of recommendation. The method models the interactions between nodes in the knowledge graph and designs a preference-aware attention mechanism to capture the user's fine-grained preference. Experiments demonstrate that the proposed method outperforms existing methods.