A Knowledge Graph Recommendation Model via High-order Feature Interaction and Intent Decomposition

Ruoyi Zhang, Huifang Ma, Qingfeng Li, Zhixin Li, Yike Wang

Abstract: Knowledge Graph(KG) contains structured attribute information which has been widely utilized for recommendations, as well as can effectively tackle the sparsity and cold start problems of collaborative filtering. In recent years, Graph Neural Networks (GNNs) serve as a novel deep learning technique that can significantly enhance recommendation performance. Unfortunately, existing KG-based GNN models are coarse-grained ignoring i)effective high-order feature interaction and fusion mechanism and ii)interpretable user latent intent decomposition. In this paper, we propose a new method named Knowledge Graph recommendation model via high-order feature Interaction and intent Decomposition(KGID), which explicitly models the fine-grained feature interaction and intent factors in KG-based GNN recommendation. Initially, high-order feature interactions are captured via the two-granularity convolutional neural networks on the item side. Next, the implicit intent factor behind the user decisions is modeled by two-level attention mechanisms. Ultimately, user representations and item representations are augmented simultaneously. We conduct experiments on three benchmark datasets to elucidate the superiority of the KGID to state-of-the-art baselines.