# Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank

arXiv: https://arxiv.org/abs/2605.11272 · HTML: https://mlsanity.com/paper/2605.11272/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 15 of 15)

- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://mlsanity.com/paper/2209.11755/) (2022)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval](https://mlsanity.com/paper/2108.08787/) (2021)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Differentiable Unbiased Online Learning to Rank](https://mlsanity.com/paper/1809.08415/) (2018)
- [On Cross-Lingual Retrieval with Multilingual Text Encoders](https://mlsanity.com/paper/2112.11031/) (2021)
- [Rankers, Judges, and Assistants: Towards Understanding the Interplay of LLMs in Information Retrieval Evaluation](https://mlsanity.com/paper/2503.19092/) (2025)
- [Fairness of Exposure in Light of Incomplete Exposure Estimation](https://mlsanity.com/paper/2205.12901/) (2022)
- [Unbiased Learning to Rank: Online or Offline?](https://mlsanity.com/paper/2004.13574/) (2020)
- [FARA: Future-aware Ranking Algorithm for Fairness Optimization](https://mlsanity.com/paper/2305.16637/) (2023)
- [Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study](https://mlsanity.com/paper/2404.03707/) (2024)
- [Towards Two-Stage Counterfactual Learning to Rank](https://mlsanity.com/paper/2506.20854/) (2025)

## Abstract (arXiv)

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 vision-language model (VLM) graded relevance labels as auxiliary supervision alongside clicks improves semantic alignment but does not preserve local content visibility. We propose a multi-objective framework combining behavioral supervision, VLM-derived relevance signals, and locale-aware boosting. Across five locales, the resulting model improves relevance while restoring stable localization, demonstrating the importance of disentangling exposure from semantic supervision.