解决跨国推荐中的内容偏见，让本地内容更易被发现。
Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank
- 用多目标框架融合点击数据与视觉语言模型的语义标签。
- 在五个增长市场中提升相关性并稳定本地内容曝光率。
- 适合做国际化推荐系统优化的研究者和工程师参考。
Adobe Express 正在拓展国际市场，但美国的内容供应和用户交互量远超其他地区。基于行为反馈训练的排序模型继承了这种不平衡：在美国流行的模板在非美地区被过度推荐。这种跨区域暴露偏差抑制了本地内容的可发现性，降低了增长市场的排序质量。我们发现仅依赖点击数据会弱化具有语义意义的定位特征。加入视觉-语言模型（VLM）生成的相关性标签作为辅助监督，虽提升了语义对齐，但未能保持本地内容可见性。为此，我们提出一种多目标框架，结合行为监督、VLM生成的相关性信号与区域感知增强机制。在五个目标地区验证表明，该模型在提升相关性的同时恢复了稳定的本地内容可见性，凸显了将曝光与语义监督解耦的重要性。
原文摘要 · Abstract (English)
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.
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