LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2302.08191v3)

Abstract: Graph neural network (GNN) is a powerful learning approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have shown superior performance in recommendation with their data augmentation schemes, aiming at dealing with highly sparse data. Despite their success, most existing graph contrastive learning methods either perform stochastic augmentation (e.g., node/edge perturbation) on the user-item interaction graph, or rely on the heuristic-based augmentation techniques (e.g., user clustering) for generating contrastive views. We argue that these methods cannot well preserve the intrinsic semantic structures and are easily biased by the noise perturbation. In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders. Our model exclusively utilizes singular value decomposition for contrastive augmentation, which enables the unconstrained structural refinement with global collaborative relation modeling. Experiments conducted on several benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the superiority of LightGCL's robustness against data sparsity and popularity bias. The source code of our model is available at https://github.com/HKUDS/LightGCL.

An Examination of LightGCL: Enhancing Graph Contrastive Learning for Recommender Systems

The paper "LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation" introduces a novel graph-based strategy to enhance recommender systems through graph neural networks (GNNs) and contrastive learning (CL). The prescribed method, LightGCL, is a promising contribution in the field of collaborative filtering, particularly with its ability to effectively harness under-utilized singular value decomposition (SVD) within the framework.

The authors characterize traditional graph contrastive learning approaches as being susceptible to biases and noise-induced perturbations when they utilize stochastic augmentation techniques such as node or edge perturbations. They argue that such methods may fail to retain intrinsic semantic structures, thereby reducing robustness and generality. LightGCL, as proposed, circumvents these limitations by leveraging SVD to facilitate contrastive augmentation, enabling a structured refinement of user-item interactions. Notably, this approach introduces minimal structural assumptions and emphasizes the understanding of global collaborative relations.

Methodological Innovations

The core innovation of LightGCL lies in its strategic use of SVD for graph augmentation. Rather than relying on heuristic manipulations of graph structure, the authors assert that SVD-based augmentation refines recommendations by distilling pertinent information from the user-item interaction matrix. Experiments indicate that this global perspective preserves essential semantics while being robust against common challenges such as data sparsity and popularity bias inherent in recommendation tasks.

The authors propose a streamlined contrastive learning model that contrasts embeddings from main view and SVD view, allowing the system to maintain its embedding training efficiency. Importantly, this reduction from three view to two views utilizing SVD representation reduces computational overhead without degrading performance.

Experimental Results

The efficacy of LightGCL is demonstrated through rigorous experimentation across multiple real-world datasets, including Yelp, Gowalla, and Amazon-book. The proposed method consistently outperforms a range of benchmark systems, including state-of-the-art models like SimGCL and SGL. For instance, LightGCL improves Recall@20 and NDCG@20 metrics significantly across datasets, affirming the strength of its augmentation strategy.

The paper also underscores LightGCL's efficiency, with batch processing complexities significantly reduced due to its simplified contrastive learning framework and preprocessing decisions strategically deferred to a one-time SVD calculation.

Implications and Future Work

LightGCL's advancements offer substantial practical implications for designing recommender systems that perform reliably under constraint conditions of data sparsity and noise. By effectively balancing between preserving local dependencies and exploiting global collaborative signals, LightGCL showcases a robust strategy that can be potentially scaled to larger and more complex datasets.

The theoretical and practical implications of LightGCL suggest trajectories for future work. Integrating causal inference could provide deeper insights into user-item interactions, further refining the augmentation process. Additionally, exploring the flexibility of using alternative matrix factorization techniques or hybrid approaches could yield further performance enhancements.

In conclusion, LightGCL presents a compellingly simple yet effective approach to graph contrastive learning in recommender systems. By leveraging SVD, it establishes a new paradigm in refining collaborative filtering methods and sets an exciting precedent for subsequent advancements in graph-based learning frameworks.