Heterogeneous Information Network-based Interest Composition with Graph Neural Network for Recommendation

TL;DR

This paper introduces HicRec, a novel graph neural network model that effectively composes user interests from multiple meta-paths in heterogeneous information networks, improving recommendation accuracy.

Contribution

HicRec employs interest composition with shared parameters and interest calculation across meta-paths, advancing HIN-based recommendation methods.

Findings

HicRec outperforms baseline models on three real-world datasets.

Interest composition improves recommendation performance.

Graph neural networks effectively model meta-path information.

Abstract

Heterogeneous information networks (HINs) are widely applied to recommendation systems due to their capability of modeling various auxiliary information with meta-paths. However, existing HIN-based recommendation models usually fuse the information from various meta-paths by simple weighted sum or concatenation, which limits performance improvement because it lacks the capability of interest compositions among meta-paths. In this article, we propose an HIN-based Interest Composition model for Recommendation (HicRec). Specifically, user and item representations are learned with a graph neural network on both the graph structure and features in each meta-path, and a parameter sharing mechanism is utilized here to ensure that the user and item representations are in the same latent space. Then, users' interests in each item from each pair of related meta-paths are calculated by a combination of the user and item representations. The composed user interests are obtained by their single interest from both intra- and inter-meta-paths for recommendation. Extensive experiments are conducted on three real-world datasets and the results demonstrate that our proposed HicRec model outperforms the baselines.