Interactive, Enhanced Dual Hypergraph Model for Explainable Contrastive Learning Recommendation

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Abstract
In recent years, it has become a hot topic to combine graph neural networks with contrastive learning, a method which has been applied not only in recommendation tasks but has also achieved impressive results in many fields, such as text processing and spatio-temporal modeling. However, existing methods are still constrained by several issues: (1) Most graph learning methods do not explore the imbalance of node and edge type distribution caused by different user–item interactions. (2) The randomness of data expansion and sampling strategies in contrastive learning may lead to confusion about the importance of key items for users. To overcome the problems, in this paper, we propose an explanation-guided contrastive recommendation model based on interactive message propagation and dual-hypergraph convolution (ECR-ID). Specifically, we designed two different interactive propagation mechanisms for the user–item dual-hypergraph sets to promote comprehensive dynamic information propagation and exchange, which further mitigates the imbalance problem of hyperedges and nodes in the hypergraph convolution, as well as the propagation loss of synergistic information between nodes. In addition, we developed an explanation-guided contrastive learning framework, which highlights the important items in user–item interactions through an explanation-based approach and guided the training of the contrastive learning framework based on the differences in the importance scores of the items, thus generating accurate positive and negative views and improving the contrastive learning performance. Finally, we integrated the contrastive learning framework with the dual-hypergraph networks based on joint training to further improve the recommendation performance. Extensive experimental evaluations on real datasets show that ECR-ID outperforms state-of-the-art recommendation algorithms. In the future, we will conduct in-depth tests based on a wider range of real-world datasets to alleviate the limitation that the existing experimental datasets all comprise data from single business services like Alibaba and Amazon, thus validating the effectiveness of our model more comprehensively.
Funding Information
  • National Natural Science Foundation of China (62472149, 62206116, 62402164, HZKY20220350, 2021CFB273, 202110500014)
  • Ministry of Education Chunhui Plan Cooperation Project (62472149, 62206116, 62402164, HZKY20220350, 2021CFB273, 202110500014)
  • Hubei Provincial Natural Science Foundation (62472149, 62206116, 62402164, HZKY20220350, 2021CFB273, 202110500014)
  • National College Student Innovation and Entrepreneurship Training Plan Program (62472149, 62206116, 62402164, HZKY20220350, 2021CFB273, 202110500014)

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