Web search has become an inevitable part of everyday life. Improving and monetizing web search has been a focus of major Internet players. Understanding the context of web search query is an important aspect of this task as it represents unobserved facts that add meaning to an otherwise incomplete query.The context of a query consists of user's location, local time, search history, behavioral segments, installed apps on their phone and so on. Queries that either explicitly use location context (eg: "best hotels in New York City") or implicitly refer to the user's physical location (e.g. "coffee shops near me") are becoming increasingly common on mobile devices. Understanding and representing the user's interest location and/or physical location is essential for providing a relevant user experience. In this study, we developed a simple and powerful neural embedding based framework to represent a user's query and their location in a single low-dimensional space. We show that this representation is able to capture the subtle interactions between the user's query intent and query/physical location, while improving the ad ranking and query-ad relevance scores over other location-unaware approaches and location-aware approaches.

翻译：网络搜索已成为日常生活中不可或缺的一部分。改进网络搜索并实现其货币化一直是主要互联网企业的关注焦点。理解网络搜索查询的上下文是这项任务的重要方面，因为它代表了未观测到的事实，这些事实为原本不完整的查询增添了意义。查询的上下文包括用户的地理位置、当地时间、搜索历史、行为细分、手机已安装应用等。在移动设备上，明确使用位置上下文（例如："纽约市最佳酒店"）或隐含指代用户物理位置（例如："我附近的咖啡店"）的查询正变得越来越普遍。理解并表征用户的兴趣位置和/或物理位置对于提供相关的用户体验至关重要。在本研究中，我们开发了一个基于神经嵌入的简洁而强大的框架，将用户的查询及其位置统一表征在单一低维空间中。我们证明，相较于其他无位置感知方法和位置感知方法，该表征能够捕捉用户查询意图与查询/物理位置之间的微妙交互，同时提升广告排序和查询-广告相关性得分。