{
  "id": 6489,
  "url": "https://arxiv.org/abs/2604.01036v1",
  "title": "Aligning Recommendations with User Popularity Preferences",
  "summary": "Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in \"rich-get-richer\" dynamics and a homogenization of visible content, but can also lead to misalignment of recommendations with individual users' preferences for popular or niche content. This work studies popularity bias through the lens of user-recommender alignment. To this end, we introduce Popularity Quantile Calibration, a measurement framework",
  "authors": "Mona Schirmer, Anton Thielmann, Pola Schwöbel, Thomas Martynec, Giuseppe Di Benedetto, Ben London et al.",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-01T15:45:24.000Z",
  "fetched_at": "2026-07-14T16:32:33.099Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6489",
  "original_url": "https://arxiv.org/abs/2604.01036v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}