HeteGraph: graph learning in recommender systems via graph convolutional networks

Dai Hoang Tran*, Quan Z. Sheng, Wei Emma Zhang, Abdulwahab Aljubairy, Munazza Zaib, Salma Abdalla Hamad, Nguyen H. Tran, Nguyen Lu Dang Khoa

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed graph convolutional networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN-based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high-quality graph’s node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. We design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show the encouraging performance of HeteGraph on the first task and the state-of-the-art performance on the second task.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusE-pub ahead of print - 8 Jan 2021

Keywords

  • Graph convolutional network
  • Heterogeneous graphs
  • Neural networks
  • Recommender systems

Fingerprint

Dive into the research topics of 'HeteGraph: graph learning in recommender systems via graph convolutional networks'. Together they form a unique fingerprint.

Cite this