Self-Supervised Hypergraph Transformer for Recommender Systems (2207.14338v1)

Abstract: Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN-based recommender systems is to recursively perform the message passing along the user-item interaction edge for refining the encoded embeddings. Despite their effectiveness, however, most of the current recommendation models rely on sufficient and high-quality training data, such that the learned representations can well capture accurate user preference. User behavior data in many practical recommendation scenarios is often noisy and exhibits skewed distribution, which may result in suboptimal representation performance in GNN-based models. In this paper, we propose SHT, a novel Self-Supervised Hypergraph Transformer framework (SHT) which augments user representations by exploring the global collaborative relationships in an explicit way. Specifically, we first empower the graph neural CF paradigm to maintain global collaborative effects among users and items with a hypergraph transformer network. With the distilled global context, a cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems. Extensive experiments demonstrate that SHT can significantly improve the performance over various state-of-the-art baselines. Further ablation studies show the superior representation ability of our SHT recommendation framework in alleviating the data sparsity and noise issues. The source code and evaluation datasets are available at: https://github.com/akaxlh/SHT.

Self-Supervised Hypergraph Transformer for Recommender Systems

The paper “Self-Supervised Hypergraph Transformer for Recommender Systems” proposed a novel framework named SHT, which integrates self-supervised learning techniques with hypergraph neural networks to enhance the robustness and generalization capabilities of recommender systems. This approach addresses the challenges of data sparsity and noise in user behavior data, which are prevalent in many practical recommendation scenarios.

Summary of Key Contributions

The core innovation of SHT lies in its ability to capture both local and global collaborative relationships through a self-supervised hypergraph transformer model, thereby augmenting user representations effectively. The paper specifies three main contributions:

1. Integration of Hypergraph Neural Networks with Transformative Architecture: By employing a topology-aware transformer network, SHT maintains global collaborative effects among user-item interactions. This integration facilitates hypergraph-guided message passing, thereby distilling auxiliary supervision signals for data augmentation.
2. Self-Supervised Augmentation: SHT introduces a cross-view generative self-supervised learning paradigm that enhances data robustness through graph topological denoising, integrating hypergraph learning with local collaborative relation encoders.
3. Extensive Empirical Evaluation: The framework demonstrates significant performance improvements over 15 distinct baseline recommender models, and shows strong capabilities to alleviate issues related to data sparsity and noise through detailed ablation studies.

Implications of Research

The implications of this work are substantial in areas where recommender systems operate with sparse and noisy user interaction data. By employing hypergraph structures, SHT can model high-order connectivity and maintain the adaptability to noisy environments. The self-supervised aspect of the framework proposes a robust regularization scheme that leverages auxiliary training signals without relying on hand-crafted labels, enhancing the model's transferability and scalability. This approach may facilitate advancements in personalized recommendations across various domains like e-commerce and multimedia platforms.

Theoretically, the work proposes a novel exploration into hypergraph-based models, pushing the boundaries of conventional graph neural networks. The use of a transformer-like architecture in hypergraph neural networks might inspire further research into similar hybrid models, possibly translating into new applications in other domains like social network analysis and knowledge graph completion.

Future Prospects

The exploration and validation of SHT open multiple pathways for future developments:

• Disentangled Representation Learning: Future work may investigate more granular representations of user intents, factoring in the potential to examine multiple customer dimensions within hypergraph structures.
• Adaptation to Temporal Dynamics: Enhancing the model to account for temporal changes in user preferences can be a promising direction, especially in time-sensitive applications.
• Expansion to Multi-Behavior Scenarios: Considering multi-dimensional user behaviors beyond single-domain interactions, SHT could evolve to capture different facets of user activities.

In conclusion, the paper introduces a well-founded framework poised to significantly leverage the capabilities of self-supervised learning within the context of hypergraph-based recommendation systems, setting the stage for future contributions to both practical applications and theoretical understanding in recommender system technology.