更好地理解Web搜索中的查询重构行为

Jia Chen, Jiaxin Mao, Yiqun Liu, Fan Zhang, M. Zhang, Shaoping Ma
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引用次数: 28

摘要

由于用户提交的查询直接影响到搜索体验,如何组织查询一直是Web搜索研究的热点。当搜索请求变得复杂和探索性时,许多搜索会话包含不止一个查询,因此需要重新表述。为了帮助用户在这些复杂的搜索任务中更好地制定查询,现代搜索引擎通常在搜索引擎结果页(serp)上提供一系列重新制定的条目,即查询建议和相关实体。然而,很少有现有的工作深入研究用户为什么以及如何在这些异构接口中执行查询重新表述。因此,搜索引擎是否为用户重新表述查询提供了足够的帮助仍有待调查。为了阐明这一研究问题,我们进行了一项实地研究,分析了细粒度的用户重构行为,包括不同搜索意图下的重构类型、入口、原因和灵感来源。与现有的依赖于外部评估者做出判断的工作不同,在实地研究中,我们收集了隐性行为信号和显性用户反馈信息。分析结果表明,Web搜索中的查询重构行为随搜索任务类型的不同而不同。我们还发现,目前搜索引擎提供的查询建议/相关查询推荐并没有为用户在复杂的搜索任务中提供足够的帮助。基于我们的实地研究结果,我们设计了一个监督学习框架来预测:1)每个查询重新表述背后的原因,以及2)用户如何组织重新表述的查询,这两者都是该领域的新挑战。这项工作提供了对Web搜索中复杂查询重新表述行为的洞察,并为在搜索引擎中设计更好的查询建议技术提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a Better Understanding of Query Reformulation Behavior in Web Search
As queries submitted by users directly affect search experiences, how to organize queries has always been a research focus in Web search studies. While search request becomes complex and exploratory, many search sessions contain more than a single query thus reformulation becomes a necessity. To help users better formulate their queries in these complex search tasks, modern search engines usually provide a series of reformulation entries on search engine result pages (SERPs), i.e., query suggestions and related entities. However, few existing work have thoroughly studied why and how users perform query reformulations in these heterogeneous interfaces. Therefore, whether search engines provide sufficient assistance for users in reformulating queries remains under-investigated. To shed light on this research question, we conducted a field study to analyze fine-grained user reformulation behaviors including reformulation type, entry, reason, and the inspiration source with various search intents. Different from existing efforts that rely on external assessors to make judgments, in the field study we collect both implicit behavior signals and explicit user feedback information. Analysis results demonstrate that query reformulation behavior in Web search varies with the type of search tasks. We also found that the current query suggestion/related query recommendations provided by search engines do not offer enough help for users in complex search tasks. Based on the findings in our field study, we design a supervised learning framework to predict: 1) the reason behind each query reformulation, and 2) how users organize the reformulated query, both of which are novel challenges in this domain. This work provides insight into complex query reformulation behavior in Web search as well as the guidance for designing better query suggestion techniques in search engines.
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