GARCIA：利用多粒度对比学习增强长尾查询的表示
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
AI总结：
本文提出 GARCIA 框架，通过基于图的知识迁移、结构增强与意图泛化的多粒度对比学习，提升服务搜索中长尾查询表示，并经离线与在线实验验证其效果。
摘要：
近年来，服务平台的发展为用户和商家都带来了极大便利，其中服务搜索引擎通过文本查询快速获取理想结果，在提升用户体验方面发挥着至关重要的作用。遗憾的是，用户不可控的搜索习惯通常会产生大量长尾查询，这严重威胁到搜索模型的能力。受近期兴起的图神经网络（GNN）和对比学习（CL）启发，已有若干研究致力于缓解长尾问题并取得了可观的性能。然而，它们仍存在一些主要不足。最重要的是，这些方法没有显式利用头部与尾部之间的上下文结构来实现有效的知识迁移，并且意图层面的信息通常被忽略，难以获得更具泛化性的表示。为此，我们提出了一种新颖框架 GARCIA，在对比学习设置中利用基于图的知识迁移和基于意图的表示泛化。具体而言，我们采用自适应编码器为查询和服务生成信息丰富的表示，并生成具有层次结构感知能力的意图表示。为充分理解尾部查询和服务，我们为 GARCIA 配备了新颖的多粒度对比学习模块，通过知识迁移、结构增强和意图泛化来增强表示。随后，完整的 GARCIA 以预训练与微调的方式得到充分训练。最后，我们在离线和在线环境中开展了大量实验，结果证明 GARCIA 在服务搜索场景中改善尾部查询和整体性能方面具有卓越能力。
英文摘要：
Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience by quickly obtaining desirable results via textual queries. Unfortunately, users' uncontrollable search customs usually bring vast amounts of long-tail queries, which severely threaten the capability of search models. Inspired by recently emerging graph neural networks (GNNs) and contrastive learning (CL), several efforts have been made in alleviating the long-tail issue and achieve considerable performance. Nevertheless, they still face a few major weaknesses. Most importantly, they do not explicitly utilize the contextual structure between heads and tails for effective knowledge transfer, and intention-level information is commonly ignored for more generalized representations. To this end, we develop a novel framework GARCIA, which exploits the graph based knowledge transfer and intention based representation generalization in a contrastive setting. In particular, we employ an adaptive encoder to produce informative representations for queries and services, as well as hierarchical structure aware representations of intentions. To fully understand tail queries and services, we equip GARCIA with a novel multi-granularity contrastive learning module, which powers representations through knowledge transfer, structure enhancement and intention generalization. Subsequently, the complete GARCIA is well trained in a pre-training&fine-tuning manner. At last, we conduct extensive experiments on both offline and online environments, which demonstrates the superior capability of GARCIA in improving tail queries and overall performance in service search scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
