A selection-pattern-aware recommendation model with colored-motif attention network

Abstract

Heterogeneous information networks (HIN) based models are now widely used to fuse auxiliary information for personalized recommendation. However, existing works mainly focus on capturing high-order connections between heterogeneous nodes through predefined pattern (such as meta-path, etc.), which depends on relevant domain knowledge and ignores the statistical characteristics of the substructures in HIN. How to capture meaningful personalized behavior patterns, especially the selection patterns (i.e. how a user select an item), and incorporate them into the preference model is still a challenge in the research of HIN-based recommendation. Therefore, in this paper, a specifically-designed Colored-Motif Attention Network (CMoAN) is proposed to deal with this problem. In the proposed CMoAN, colored motif are used as the elemental building blocks to represent context-based selection patterns, and then by constructing a motif-based adjacency matrix to capture higher-order semantic association between nodes in HIN. Besides, an attentive graph neural network is designed to efficiently model the semantics and high-order relations information. Extensive experiments on three real-world datasets demonstrate that CMoAN consistently outperforms state-of-the-art methods. Furthermore, experimental results also verify the effectiveness of using colored motif for capturing users’selection patterns.