Meta-path automatically extracted from heterogeneous information network for recommendation

Abstract

Heterogeneous information networks have been proven to effectively improve recommendations due to their diverse information content. However, there are still two challenges for recommendation methods based on heterogeneous information networks. To begin with, current methods often depend on experts to manually craft meta-paths, and it can be challenging to define an adequate set of meta-paths for complex task scenarios. Second, most models fail to fully explore user preferences for paths or meta-paths whileimultaneously learning path or meta-path explicit representations. To tackle the aforementioned issues, we propose a model for recommendation utilizing meta-path automatically extracted from heterogeneous information network, called MAERec. Specifically, MAERec employs an automatic approach to extract high-quality path instances from heterogeneous information networks and construct meta-paths. These meta-paths are then utilized by a hierarchical attention network to learn an explicit representation of the meta-path-based context. Extensive experiments conducted on various real-world datasets not only showcase the superior performance of MAERec when compared to state-of-the-art methods but also underscore its capability to automatically discover high-quality path instances for meta-path extraction.