Latent Factor Models vs. Collaborative Filtering in Large-Scale Music Recommendation Efficiency

Abstract

Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the inference efficiency of latent factor models scale with dataset size in music recommendation compared to collaborative filtering methods when evaluated on metrics like throughput and. Music recommender systems have become central parts of popular streaming platforms such as Last.fm, Pandora, or Spotify to help users find music that fits their preferences. These systems learn from the past listening events of users to recommend music a user will likely listen. 9 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 3.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the inference efficiency of latent factor models scale with dataset size in music recommendation compared to collaborative filtering methods when evaluated on metrics like throughput and memory footprint on large-scale benchmarks like Spotify or KKBox?

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