LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation

By Xuheng Cai, Chao Huang, Lianghao Xia, Xubin Ren. International Conference on Learning Representations, 2023

395 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/1b06e507f7e2af3dc7cf28086ac34083709c3150

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lianghao Xia
2. Chao Huang
3. Suqi Cheng
4. Xubin Ren
5. Wei Wei
6. Lixin Su
7. Junfeng Wang
8. Dawei Yin

In Information Systems

1. Lianghao Xia
2. Chao Huang
3. Suqi Cheng
4. Xubin Ren
5. Wei Wei
6. Lixin Su
7. Junfeng Wang
8. Dawei Yin

Citations in the corpus

• Representation Learning with Large Language Models for Recommendation (2023, methodology): “To further refine the recommendations, we integrate the textual information of each item using our custom prompts (for prompt format details, please refer to Appendix A.3).”