Connecting Latent ReLationships over Heterogeneous Attributed Network for Recommendation

TL;DR

This paper introduces HANRec, a novel GNN-based model designed to effectively capture heterogeneity and latent relationships in attributed heterogeneous networks for improved recommendation performance.

Contribution

The paper proposes HANRec, a new GNN model that models heterogeneity and latent correlations in attributed networks for recommendation systems.

Findings

HANRec outperforms state-of-the-art methods on real-world datasets.

It effectively captures heterogeneity and latent relationships.

The model improves recommendation accuracy.

Abstract

Recently, deep neural network models for graph-structured data have been demonstrating to be influential in recommendation systems. Graph Neural Network (GNN), which can generate high-quality embeddings by capturing graph-structured information, is convenient for the recommendation. However, most existing GNN models mainly focus on the homogeneous graph. They cannot characterize heterogeneous and complex data in the recommendation system. Meanwhile, it is challenging to develop effective methods to mine the heterogeneity and latent correlations in the graph. In this paper, we adopt Heterogeneous Attributed Network (HAN), which involves different node types as well as rich node attributes, to model data in the recommendation system. Furthermore, we propose a novel graph neural network-based model to deal with HAN for Recommendation, called HANRec. In particular, we design a component connecting potential neighbors to explore the influence between neighbors and provide two different strategies with the attention mechanism to aggregate neighbors' information. The experimental results on two real-world datasets prove that HANRec outperforms other state-of-the-art methods.