LightGCL, SGL, and GCA Robustness Under Varying Data Sparsity in Recommender Systems

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the relative robustness of LightGCL versus SGL and GCA under varying levels of data sparsity in user-item interactions, as measured by precision@10 and recall@20 on synthetic. Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the relative robustness of LightGCL versus SGL and GCA under varying levels of data sparsity in user-item interactions, as measured by precision@10 and recall@20 on synthetic sparsity-perturbed versions of MovieLens-100K?

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