AI 中文总结
研究苹果音乐多语言搜索问题，提出基于暹罗双编码器并微调的多语言语义检索系统，经混合检索架构集成。该系统离线和在线测试效果良好，提升了搜索召回率，尤其改善了难查询的表现，是平台较大的搜索质量改进。
AI 中文摘要
苹果音乐为全球150多个店面的听众提供服务，语种众多且曲目每日剧增。在此规模下，对拼写错误、音译和跨语言查询的搜索召回率成为会话质量的主要驱动力。本文提出一种多语言语义检索系统，基于305M参数的暹罗双编码器，从GTE多语言基础模型微调而来，并采用课程安排的多目标训练。该模型通过混合检索架构集成到搜索堆栈中，离线时，在Hit@10指标上比GTE多语言基础模型有69%的相对提升。在线A/B测试中，系统整体转化率提升2.28%，无结果率降低86%，各店面均有提升且无回归现象。语义检索有效提高了难查询的召回率，而不影响热门查询。这是该平台部署的最大搜索质量改进之一。
英文摘要
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.
CommentsAccepted to the Industry Track of the 20th ACM Conference on Recommender Systems (RecSys 2026)
