Hyper meta-path contrastive learning for multi-behavior recommendation

Haoran YANG, Hongxu CHEN, Lin LI, Philip S. YU, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

29 Citations (Scopus)

Abstract

User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via leveraging the advantages of graph neural networks (GNNs) or multi-task learning. However, most existing works do not take the complex dependencies among different behaviors of users into consideration. They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks. To tackle the challenge, in this paper, we first propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. How to obtain a unified embedding for a user from hyper meta-paths and avoid the previously mentioned limitations simultaneously is critical. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors. A new graph contrastive learning based framework is proposed by coupling with hyper meta-paths, namely HMG-CR, which consistently and significantly outperforms all baselines in extensive comparison experiments. Copyright © 2021 IEEE.

Original languageEnglish
Title of host publicationProceedings of 21st IEEE International Conference on Data Mining
EditorsJames BAILEY, Pauli MIETTINEN, Yun Sing KOH, Dacheng TAO, Xindong WU
Place of PublicationDanvers, MA
PublisherIEEE
Pages787-796
ISBN (Electronic)9781665423984
DOIs
Publication statusPublished - 2021

Citation

Yang, H., Chen, H., Li, L., Yu, P. S., & Xu, G. (2021). Hyper meta-path contrastive learning for multi-behavior recommendation. In J. Bailey, P. Miettinen, Y. S. Koh, D. Tao, & X. Wu (Eds.), Proceedings of 21st IEEE International Conference on Data Mining (pp. 787-796). IEEE. https://doi.org/10.1109/ICDM51629.2021.00090

Keywords

  • Hyper meta-path
  • Graph contrastive learning
  • Recommendation systems

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