HeteGraph: a convolutional framework for graph learning in recommender systems

Dai Hoang Tran, Abdulwahab Aljubairy, Munazza Zaib, Quan Z. Sheng, Wei Emma Zhang, Nguyen H. Tran, Khoa L. D. Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

5 Citations (Scopus)


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. For evaluation, 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 our HeteGraph's encouraging performance on the first task and state-of-the-art performance on the second task.

Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781728169262
Publication statusPublished - 2020
Event2020 International Joint Conference on Neural Networks - Virtual, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameIEEE International Joint Conference on Neural Networks (IJCNN)
ISSN (Print)2161-4393


Conference2020 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2020
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow


  • Heterogeneous Graph
  • Recommender System
  • Graph Convolutional Network


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

Cite this