Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success in web search. However, many state-of-the-art text ranking approaches only focus on core relevance while ignoring other dimensions that contribute to user satisfaction, e.g., document quality, recency, authority, etc. In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner. In particular, we leverage the capacities of PLMs on both textual and numerical inputs, and apply a multi-field input that modularizes each dimension of user satisfaction as an input field. Overall, SAT-Ranker is an effective, extensible, and data-centric framework that has huge potential for industrial applications. On rigorous offline and online experiments, SAT-Ranker obtains remarkable gains on various evaluation sets targeting different dimensions of user satisfaction. It is now fully deployed online to improve the usability of our search engine.

翻译：搜索引擎在满足用户多样的信息需求中扮演着关键角色。近年来，基于预训练语言模型（PLMs）的文本排序模型在网页搜索领域取得了巨大成功。然而，许多先进的文本排序方法仅关注核心相关性，而忽视了影响用户满意度的其他维度（例如文档质量、时效性、权威性等）。在本工作中，我们聚焦于网页搜索中用户满意度的排序问题而非相关性排序，并提出了一个基于PLM的框架——SAT-Ranker，该框架以统一方式全面建模用户满意度的不同维度。具体而言，我们利用PLM在文本与数值输入上的能力，采用多字段输入方法，将用户满意度的每个维度模块化为一个输入字段。总体而言，SAT-Ranker是一个有效、可扩展且以数据为中心的框架，在工业应用中具有巨大潜力。通过严格的离线和在线实验，SAT-Ranker在针对用户满意度不同维度的多个评估集上取得了显著提升。目前该框架已全面部署上线，以提升我们搜索引擎的可用性。