Aggregating knowledge-aware graph neural network and adaptive relational attention for recommendation

Currently, recommender systems based on knowledge graph (KG) consider various aspects of the item to provide accurate recommendations. Many studies have shown that exploiting the rich semantics of KG is effective to improve recommendation performance, and can solve data sparsity and provide interpretable recommendation. However, most existing KG-based recommender systems ignore the fact that users attach different degrees of importance to various relationships of items. To tackle this problem, we propose a knowledge graph recommender model based on adaptive relational attention (KGARA), which can capture the attention of various users to different relationships of items. Specifically, we introduce the relation embedding to model the semantic information of KG, and capture the user's attention on each relation of the targeted item with the attention mechanism. In addition, we introduce receptive fields to select neighbor nodes of the target node in the KG, which greatly alleviate computational burden. Extensive experiments on three real-world datasets demonstrate that the proposed algorithm has significant improvements over other state-of-the-art algorithms.