AutoML vs. Manual Architectures in Large-Scale Sequential Recommendation Efficiency

This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the inference efficiency of AutoML-designed recommender architectures compare to manual designs when evaluated on large-scale sequential recommendation benchmarks. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the inference efficiency of AutoML-designed recommender architectures compare to manual designs when evaluated on large-scale sequential recommendation benchmarks?

Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.