Adaptive Graph Contrastive Learning for Recommendation (2305.10837v3)

Abstract: Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing along user-item interaction edges to refine encoded embeddings, relying on sufficient and high-quality training data. However, user behavior data in practical recommendation scenarios is often noisy and exhibits skewed distribution. To address these issues, some recommendation approaches, such as SGL, leverage self-supervised learning to improve user representations. These approaches conduct self-supervised learning through creating contrastive views, but they depend on the tedious trial-and-error selection of augmentation methods. In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators to better empower the CF paradigm. Specifically, we use two trainable view generators - a graph generative model and a graph denoising model - to create adaptive contrastive views. With two adaptive contrastive views, AdaGCL introduces additional high-quality training signals into the CF paradigm, helping to alleviate data sparsity and noise issues. Extensive experiments on three real-world datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods. Our model implementation codes are available at the link https://github.com/HKUDS/AdaGCL.

Adaptive Graph Contrastive Learning for Recommendation: An Evaluation

The paper "Adaptive Graph Contrastive Learning for Recommendation" introduces a novel framework aimed at enhancing collaborative filtering (CF) models in recommender systems. The authors address crucial challenges inherent in graph neural network-based CF models, particularly related to data noise, sparsity, and the often-skewed distribution in practical user behavior data. They propose an Adaptive Graph Contrastive Learning (AdaGCL) method to overcome these challenges by integrating two adaptive contrastive view generators, thereby introducing high-quality training signals to improve the robustness and effectiveness of CF models.

Graph neural networks (GNNs) have become a significant method in CF paradigms, owing to their ability to refine user-item interaction embeddings by propagating information along interaction edges. However, this paper argues that while existing models such as SGL utilize self-supervised learning with contrastive views, they fall short due to trial-and-error selection of augmentation methods, which can be tedious and potentially limit performance. AdaGCL addresses this by employing two trainable view generators: a graph generative model and a graph denoising model. The incorporation of these two models provides adaptive contrastive views that introduce additional high-quality training signals and alleviate the issues of data sparsity and noise.

Through extensive experimentation on three real-world datasets—Last.FM, Yelp, and BeerAdvocate—the authors demonstrate that AdaGCL outperforms various state-of-the-art models, such as LightGCN, SGL, and NCL, particularly in handling data noise and sparsity. This superiority is attributed to the framework's ability to generate informative and diverse contrastive views without relying on random data augmentations. It is noteworthy that AdaGCL not only improves the robustness of the CF models in noisy scenarios but also showcases enhanced performance in sparse data conditions. The paper’s statistical significance tests support these claims, indicating substantial improvements in recommendation metrics when compared to established baselines.

The implications of this research are substantial for both practical applications and theoretical advancements in AI-based recommendation systems. By leveraging adaptive contrastive learning, AdaGCL presents a pathway to improved modeling of user behavior and preferences, even in challenging data environments. Practically, this means consumers can expect more accurate and relevant recommendations with reduced impact from anomalous user interactions or noise. Theoretically, AdaGCL contributes to the growing research area focused on self-supervised learning and contrastive learning in graph-based systems, specifically by intentionally designing adaptive view generation mechanisms that accommodate data distributions.

For future developments in AI, particularly in recommendation systems, the concept of adaptive contrastive learning introduced by AdaGCL can serve as a foundational strategy to enhance model robustness and generalization capabilities. As self-supervised learning techniques evolve, exploring the integration of causal inference and transfer learning can further extend AdaGCL's applicability, enabling models not only to learn from data more effectively but also to generalize across domains and tasks.

In summary, the paper presents a methodologically sound and empirically validated framework for improving graph-based CF recommender systems. AdaGCL leverages graph generative and denoising models to generate adaptive contrastive views, enhancing the learning process through self-supervised signals and addressing the challenges of data noise and sparsity in practical scenarios. The results indicate a promising direction for future research and applications in adaptive and robust recommendation frameworks.