LightGCL and GNN-Based Recommendation Models: A Contrastive Learning Benchmark Study

This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the contrastive learning performance of LightGCL compare to other GNN-based recommendation models (e.g., SGL, GCA) when evaluated on standard benchmarks like MovieLens-100K and Amazon Book. 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. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the contrastive learning performance of LightGCL compare to other GNN-based recommendation models (e.g., SGL, GCA) when evaluated on standard benchmarks like MovieLens-100K and Amazon Book Reviews in terms of precision@10 and recall@20?

Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.