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HeteGraph: graph learning in recommender systems via graph convolutional networks

  • S.I. : Deep Social Computing
  • Published:
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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.

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Availability of data and material

Public dataset MovieLens is available at: https://grouplens.org/datasets/movielens

Public dataset Book-Crossing is available at: www2.informatik.uni-freiburg.de/~cziegler/BX.

Code availability

The source code of HeteGraph is available at: http://github.com/heroddaji/dai_hetegraph.

Notes

  1. https://grouplens.org/datasets/movielens.

  2. https://grouplens.org/datasets/book-crossing.

  3. https://www.imdb.com.

  4. https://www.themoviedb.org.

  5. https://www.amazon.com.

  6. https://www.wikidata.org.

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Funding

This study was funded by Australian Research Council Discovery Project ARC, Grant number DP200102298.

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Authors and Affiliations

  1. Macquarie University, Sydney, Australia

    Dai Hoang Tran, Quan Z. Sheng, Abdulwahab Aljubairy, Munazza Zaib & Salma Abdalla Hamad

  2. The University of Adelaide, Adelaide, Australia

    Wei Emma Zhang

  3. The University of Sydney, Sydney, Australia

    Nguyen H. Tran

  4. CSIRO Data61, Sydney, Australia

    Nguyen Lu Dang Khoa

Authors
  1. Dai Hoang Tran
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  2. Quan Z. Sheng
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Correspondence to Dai Hoang Tran.

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Author Quan Z. Sheng has received research grants from Company Australian Research Council.

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Tran, D.H., Sheng, Q.Z., Zhang, W.E. et al. HeteGraph: graph learning in recommender systems via graph convolutional networks. Neural Comput & Applic 35, 13047–13063 (2023). https://doi.org/10.1007/s00521-020-05667-z

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