Self-supervised Graph Learning for Recommendation (2010.10783v4)

Abstract: Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage and LightGCN. Despite effectiveness, we argue that they suffer from two limitations: (1) high-degree nodes exert larger impact on the representation learning, deteriorating the recommendations of low-degree (long-tail) items; and (2) representations are vulnerable to noisy interactions, as the neighborhood aggregation scheme further enlarges the impact of observed edges. In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation. The idea is to supplement the classical supervised task of recommendation with an auxiliary self-supervised task, which reinforces node representation learning via self-discrimination. Specifically, we generate multiple views of a node, maximizing the agreement between different views of the same node compared to that of other nodes. We devise three operators to generate the views -- node dropout, edge dropout, and random walk -- that change the graph structure in different manners. We term this new learning paradigm as \textit{Self-supervised Graph Learning} (SGL), implementing it on the state-of-the-art model LightGCN. Through theoretical analyses, we find that SGL has the ability of automatically mining hard negatives. Empirical studies on three benchmark datasets demonstrate the effectiveness of SGL, which improves the recommendation accuracy, especially on long-tail items, and the robustness against interaction noises. Our implementations are available at \url{https://github.com/wujcan/SGL}.

• The paper introduces Self-Supervised Graph Learning (SGL), a method that augments graph node views to boost recommendation accuracy and mitigate noise.
• It devises three data augmentation operators—node dropout, edge dropout, and random walk—to improve graph representations and accelerate training convergence.
• Empirical results on benchmark datasets show significant gains in recall, NDCG, and efficiency compared to traditional GCN-based recommendation models.

Self-Supervised Graph Learning for Recommendation

Representation learning on user-item graphs has become a pivotal area of research in recommender systems, particularly with the advent of Graph Convolutional Networks (GCNs). This paper, authored by Wu et al., critically examines the current state-of-the-art GCN models for recommendation tasks, such as PinSage and LightGCN, identifying notable limitations, and proposes a novel self-supervised learning approach to address these shortcomings.

Key Contributions

The paper makes several significant contributions to the field of recommendation systems:

1. Introduction of Self-Supervised Graph Learning (SGL): The authors introduce a new learning paradigm—Self-Supervised Graph Learning (SGL)—which supplements the main supervised task of recommendation with an auxiliary self-supervised task. This auxiliary task involves generating multiple views of nodes in a user-item graph and maximizing the agreement between different views of the same node while discriminating between views of different nodes.
2. Data Augmentation Operators: The paper devises three operators for generating multiple views of nodes:
   • Node Dropout: Randomly removing nodes and their associated edges.
   • Edge Dropout: Randomly removing edges while keeping the nodes intact.
   • Random Walk: Constructing graph structure variations via random walks, allowing recovery of higher-order connectivity even for sparse nodes.
3. Integration with LightGCN: By implementing these augmentations on LightGCN, the paper demonstrates how SGL can improve recommendation accuracy and robustness against noisy interactions.
4. Theoretical Analysis: The authors provide a theoretical analysis highlighting that SGL inherently facilitates hard negative mining, further improving learning efficiency and performance.

Empirical Results

The empirical studies conducted across three benchmark datasets—Yelp2018, Amazon-Book, and Alibaba-iFashion—demonstrate that SGL notably outperforms LightGCN and other baseline models. The key findings include:

• Recommendation Accuracy: SGL exhibits significant improvements in recall and NDCG metrics, especially for long-tail items, which typically suffer from insufficient interaction data.
• Robustness to Noise: The proposed SGL method shows robustness to noisy interactions, an essential feature given the prevalence of implicit feedback in real-world scenarios.
• Training Convergence: SGL enables faster convergence during training compared to traditional GCN models, attributed to its enhanced ability to mine hard negatives and thus provide more meaningful training signals.

Implications and Future Directions

Practical Implications

1. Enhanced Recommendation Systems: Implementing SGL can lead to more accurate and robust recommendation systems capable of better handling long-tail distributions and noisy data, common challenges in practical applications.
2. Scalability: Given the ability of SGL to accelerate convergence, it offers a pathway toward more scalable recommender systems that can efficiently process large-scale interaction data.

Theoretical Implications

1. Self-Supervised Learning: This work underscores the potential of self-supervised learning in domains beyond its traditional use-cases in computer vision and natural language processing. Its application to graph-based representation learning opens a new avenue for research.
2. Graph Representation Learning: The introduction of data augmentation strategies tailored for graph structures can inspire further research into more sophisticated augmentation techniques that capture intricate structural relationships.

Speculative Future Directions

1. Pre-Training and Transfer Learning: Future research could explore the development of pre-trained models on large user-item graphs that can be fine-tuned for specific recommendation tasks in various domains, leveraging the transferability of representations learned through SSL.
2. Hybrid Approaches: Integrating SSL with other state-of-the-art methods, such as attention mechanisms or meta-learning approaches, might yield further improvements in recommendation performance by capturing additional dimensions of user-item interactions.
3. Theoretical Extensions: Expanding the theoretical framework to analyze the impact of different types of data augmentations on graph structures could provide deeper insights into optimizing SSL approaches for various graph-based tasks.

Conclusion

This paper by Wu et al. presents a robust improvement in the domain of graph-based recommendation by integrating self-supervised learning. Self-Supervised Graph Learning (SGL) mitigates the limitations of existing GCN models, particularly concerning long-tail item recommendations and robustness to interaction noise. The empirical and theoretical contributions of this work pave the way for further advancements in the field, offering both practical and theoretical paths for future exploration. The implementation and open-source availability of SGL provide a valuable resource for continued research and development in recommendation systems.

By exploring these advancements, the paper enhances our understanding and application of self-supervised learning in recommender systems, making a substantial contribution to the ongoing development of more effective, efficient, and scalable recommendation technologies.