基于在线用户反馈的统一搜索联合系统

Luo Jie, Sudarshan Lamkhede, Rochit Sapra, Evans Hsu, Helen Song, Yi Chang
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引用次数: 20

摘要

今天流行的网络搜索引擎将搜索过程从抓取网页扩展到专门的语料库(“垂直”),如图像,视频,新闻,本地,体育,金融,购物等,每个都有自己的专业搜索引擎。搜索联合处理选择搜索引擎进行查询并将其结果合并到单个结果集中的问题。尽管最近取得了一些进展,但这个问题仍然非常具有挑战性。首先,由于不同垂直方向的异构性,系统如何将垂直方向的结果与web文档合并以满足用户的信息需求仍然是一个有待解决的问题。此外,搜索引擎的规模和越来越多的垂直属性需要一个高效和可扩展的解决方案。在本文中,我们提出了一个搜索联合问题的统一框架。我们将搜索联盟建模为上下文强盗问题。该系统使用奖励作为用户满意度的代理。给定一个查询,我们的系统预测每个垂直的预期奖励,然后以最大化总奖励的方式组织搜索结果页面(SERP)。我们的系统不依赖于人类的判断,而是利用隐含的用户反馈来学习模型。该方法实现效率高,可应用于不同性质的垂线。我们已经成功地将该系统部署到三个不同的市场,它可以处理每个市场的多个垂直市场。该系统现在每天实时处理数以亿计的查询,并大大提高了用户指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A unified search federation system based on online user feedback
Today's popular web search engines expand the search process beyond crawled web pages to specialized corpora ("verticals") like images, videos, news, local, sports, finance, shopping etc., each with its own specialized search engine. Search federation deals with problems of the selection of search engines to query and merging of their results into a single result set. Despite a few recent advances, the problem is still very challenging. First, due to the heterogeneous nature of different verticals, how the system merges the vertical results with the web documents to serve the user's information need is still an open problem. Moreover, the scale of the search engine and the increasing number of vertical properties requires a solution which is efficient and scaleable. In this paper, we propose a unified framework for the search federation problem. We model the search federation as a contextual bandit problem. The system uses reward as a proxy for user satisfaction. Given a query, our system predicts the expected reward for each vertical, then organizes the search result page (SERP) in a way which maximizes the total reward. Instead of relying on human judges, our system leverages implicit user feedback to learn the model. The method is efficient to implement and can be applied to verticals of different nature. We have successfully deployed the system to three different markets, and it handles multiple verticals in each market. The system is now serving hundreds of millions of queries live each day, and has improved user metrics considerably.
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