CAREER: Making Aggregated Search Results More Effective and Useful

职业：使聚合搜索结果更有效、更有用

• 批准号: 1451668
• 负责人: Jaime Arguello
• 金额: $ 51.87万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451668&HistoricalAwards=false
• 关键词: CAREER; Making; Aggregated; Search; Results

Aggregated search is the task of combining results from multiple independent search engines into a single presentation. The most widely used aggregated search systems are commercial search portals such as Google. In addition to web search, commercial search portals provide access to a wide range of auxiliary search services, or verticals, that focus on a specific type of media (e.g., images, videos) or search task (e.g., search for news, local businesses). Aggregated search systems are responsible for predicting which verticals to present (Does the user want to see images or news?) and where/how to present them. This project will study a phenomenon called aggregated search coherence and its effect on search behavior. Given an ambiguous query (e.g., "saturn"), a common strategy for a search engine is to diversify its results (e.g., to return results about the car and the planet). Aggregated search coherence is the extent to which results from different sources focus on similar senses of the query. The outcomes of this project will provide the knowledge required to expand the accessibility of search across a wide range of domains. The test collection produced will allow others to reproduce the results of this work and test their own solutions. The software will enable others to perform large-scale remote studies of search behavior. Insights gained from the user studies will be of interest to researchers in other fields such as psychology and marketing.Prior research by the PI found that the query-senses in the vertical results can affect user interaction with other components on the aggregated results page, the so-called "spill-over" effect. This project will investigate how aggregated search coherence affects search behavior and will incorporate this knowledge into new methods for aggregated search evaluation and prediction. Specifically, four objectives will be tackled. (1) A series of user studies will be conducted to investigate how different factors of the user, the search task, the results presentation, and the layout determine the level of spill-over from one component to another. (2) Using the insights gained from these studies, a new test-collection evaluation methodology will be developed and validated that models cross-component effects. (3) Due to the pipeline architecture of existing systems, results from different components are completely independent of each other. New algorithms will be developed and evaluated for predicting which results from each component to display and how. The goal will be to minimize negative cross-component effects. (4) The generalizability of the methods will be tested on two additional domains: library search and news story aggregation. Aggregated search facilitates single-query access to different types of media, which require customized search solutions. It is the underlying technology behind commercial search portals and also widely used in other domains such as library, mobile, and desktop search. The project will study a phenomenon that is not currently well-understood, nor addressed in existing evaluation methods and algorithmic solutions for aggregated search.

聚合搜索是将来自多个独立搜索引擎的结果组合到单个表示中的任务。最广泛使用的聚合搜索系统是谷歌等商业搜索门户。除了网络搜索之外，商业搜索门户还提供对广泛的辅助搜索服务或垂直搜索服务的访问，这些服务侧重于特定类型的媒体（例如，图像，视频）或搜索任务（例如，搜索新闻，本地企业）。聚合搜索系统负责预测要呈现哪些垂直内容（用户想要看到图片还是新闻？）以及在哪里/如何呈现它们。这个项目将研究一种被称为聚合搜索一致性的现象及其对搜索行为的影响。给定一个模棱两可的查询（例如，“土星”），搜索引擎的一个常见策略是使其结果多样化（例如，返回关于汽车和行星的结果）。聚合搜索一致性是指来自不同来源的结果集中在查询的相似意义上的程度。该项目的成果将提供在广泛领域扩展搜索可访问性所需的知识。生成的测试集合将允许其他人复制这项工作的结果并测试他们自己的解决方案。该软件将使其他人能够对搜索行为进行大规模的远程研究。从用户研究中获得的见解将对心理学和市场营销等其他领域的研究人员感兴趣。PI先前的研究发现，垂直结果中的查询感觉会影响用户与聚合结果页面上其他组件的交互，即所谓的“溢出”效应。该项目将研究聚合搜索一致性如何影响搜索行为，并将这些知识整合到聚合搜索评估和预测的新方法中。具体而言，将处理四个目标。(1)将进行一系列用户研究，以调查用户、搜索任务、结果呈现和布局的不同因素如何决定从一个组件到另一个组件的溢出水平。(2)利用这些研究获得的见解，将开发并验证一种新的测试集评估方法，该方法可以模拟交叉成分效应。(3)由于现有系统的流水线架构，不同组件的结果是完全独立的。将开发和评估新的算法，以预测每个组件的哪个结果要显示以及如何显示。目标将是尽量减少负面的交叉成分影响。(4)方法的泛化性将在另外两个领域进行测试：图书馆搜索和新闻故事聚合。聚合搜索促进了对不同类型媒体的单一查询访问，这需要定制的搜索解决方案。它是商业搜索门户背后的基础技术，也广泛用于其他领域，如图书馆、移动和桌面搜索。该项目将研究一种现象，这种现象目前还没有得到很好的理解，也没有在现有的评估方法和聚合搜索算法解决方案中得到解决。