提升苹果音乐跨语言搜索准确率，尤其改善冷门查询体验。
Multilingual Semantic Retrieval for Apple Music Search
- 用305M参数双塔模型融合多任务训练，增强跨语言语义理解。
- 线上测试整体转化率提升2.28%，无结果率下降86%。
- 对冷门查询效果显著，转化率提升7.93%，适合跨境内容检索场景。
Apple Music 在150多个地区以数十种语言服务用户，每日新增数以十万计的新曲目。在如此规模下，拼写错误、音译和跨语言查询的召回率成为影响会话质量的关键因素，尤其是占大多数的尾部查询。本文提出一个基于305M参数双塔模型（从GTE-multilingual-base微调）的多语言语义检索系统，采用课程式调度的多目标训练方法。该模型通过混合检索架构集成至搜索流程，将密集向量近邻结果与现有基于词元的索引结合，利用分位数分布匹配实现部署，无需重新训练下游排序模型。离线评估显示，相比GTE-multilingual-base，Hit@10相对提升69%。全球在线A/B测试中，系统带来整体转化率2.28%的相对提升，无结果率降低86%，所有地区均获收益且无负向影响。性能提升集中于最需要改进的尾部查询：其转化率提升7.93%，远高于中频查询的0.89%和头部查询的0.14%，证明该系统有效提升困难查询召回率，同时不干扰热门内容的精准服务。据我们所知，这是该平台部署的最大规模搜索质量改进。
原文摘要 · Abstract (English)
Apple Music serves listeners across 150+ storefronts in dozens of languages, with a catalog that grows by hundreds of thousands of new tracks daily. At this scale, search recall on misspelled, transliterated, and cross-lingual queries becomes a dominant driver of session quality, particularly for tail queries that account for the majority of unique queries. We present a multilingual semantic retrieval system built on a 305M-parameter Siamese bi-encoder fine-tuned from GTE-multilingual-base with curriculum-scheduled multi-objective training. The model is integrated into the search stack via a hybrid retrieval architecture that blends dense nearest-neighbor results with the existing token-based index using quantile distribution matching, enabling deployment without retraining downstream rankers. Offline, the model achieves a 69% relative improvement in Hit@10 over GTE-multilingual-base. In a worldwide online A/B test, the system delivers a 2.28% relative conversion-rate (CR) lift overall, an 86% reduction in the no-result rate, and gains across every storefront with no observed regressions. The improvement is concentrated where it is needed most: tail queries see a 7.93% relative CR lift, compared with 0.89% for mid-frequency queries and 0.14% for head queries -- evidence that semantic retrieval improves recall on hard queries without disturbing well-served popular ones. To our knowledge, this is one of the largest search-quality improvements deployed on the platform.
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