{
  "id": 4527,
  "url": "https://arxiv.org/abs/2605.11272v1",
  "title": "Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank",
  "summary": "Adobe Express is expanding internationally, but the US has a disproportionately large content supply and interaction volume. Learning-to-rank (LTR) models trained primarily on behavioral feedback inherit this imbalance: templates popular in US are over-served in non-US locales. This cross-locale exposure bias suppresses local content discoverability and degrades ranking quality in growth locales. We show that click-only training suppresses semantically informative localization features. Adding v",
  "authors": "Suryaa Veerabathiran Seran, Ashwin Naresh Kumar, Tracy Holloway King, Jing Zheng",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T21:51:13.000Z",
  "fetched_at": "2026-07-14T16:31:03.580Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4527",
  "original_url": "https://arxiv.org/abs/2605.11272v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}