Cross-Domain Performance of LightGCL, SGL, and GCA in Recommendation Systems

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the performance of LightGCL, SGL, and GCA vary when applied to cross-domain recommendation tasks, such as transferring from MovieLens-100K to Amazon Book Reviews, in terms of precision@10. In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.7/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the performance of LightGCL, SGL, and GCA vary when applied to cross-domain recommendation tasks, such as transferring from MovieLens-100K to Amazon Book Reviews, in terms of precision@10 and recall@20?

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