LightGCL vs. GNN-Based Contrastive Learning Methods on Noisy Recommendation Benchmarks

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does the performance of LightGCL compare to other GNN-based contrastive learning methods (e.g., SGL, GCA) on standard recommendation benchmarks (e.g., MovieLens, Amazon) when evaluated under. 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. 10 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.1/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the performance of LightGCL compare to other GNN-based contrastive learning methods (e.g., SGL, GCA) on standard recommendation benchmarks (e.g., MovieLens, Amazon) when evaluated under varying levels of noisy user-item interactions?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance