EAGER: Learning to Efficiently Rank with Cascades

EAGER：学习使用级联进行有效排名

• 批准号: 1144034
• 负责人: Jimmy Lin
• 金额: $ 15万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144034&HistoricalAwards=false
• 关键词: EAGER; Learning; Efficiently; Rank; Cascades

Text search is undeniably vital to today's information-based societies, helping users locate relevant information in web pages, journal articles, news stories, blogs, emails, tweets, and a myriad of other sources. Naturally, users desire results that are not only good but also fast. Learning to rank, the dominant approach to information retrieval (IR) today, focuses almost exclusively on effectiveness, often neglecting the runtime speed (i.e., efficiency) of the ranking functions. This project contributes to the emerging research area of learning to efficiently rank, which aims to let algorithm designers capture, model, and reason about tradeoffs between effectiveness and efficiency in a unified framework. Specifically, this project explores a novel cascade model for retrieval, where ranking is broken into a finite number of distinct stages. Each stage considers successively richer and more complex features, but over successively smaller candidate document sets. The intuition is that although complex features are more time-consuming to compute, examining fewer documents offsets the additional overhead. In other words, the cascade model views retrieval as a multi-stage progressive refinement problem. Based on the survey of the current state-of-the-art, knowledge, this is the first project to explore this approach to the ranking problem, marking a substantial departure from previous "monolithic" ranking functions. Although exploration in this uncharted area carries some risk, this research promises to open up a new frontier in IR research. This project aims to narrow the chasm between academic and industrial IR research by bringing together theoretical IR research and practical considerations in "real-world" search. It is expected that the cascade model will be of interest to web search engine companies, thus providing a path from the exploratory research results to significant impact in production systems. Furthermore, this work dovetails with the emerging area of green computing: more efficient algorithms use less energy, hence help reduce the environmental footprint of web-scale services. The project web site (http://www.umiacs.umd.edu/~jimmylin/projects/) includes more information about this project and will be used for the release of a prototype as part of the Ivory open-source retrieval toolkit.

不可否认，文本搜索对当今的信息社会至关重要，它可以帮助用户在网页、期刊文章、新闻故事、博客、电子邮件、推特和无数其他来源中查找相关信息。当然，用户希望得到的结果不仅好而且快。学习排序是当今信息检索(IR)的主要方法，几乎只关注有效性，往往忽略了排序函数的运行速度(即效率)。这个项目对学习有效排名这一新兴研究领域做出了贡献，旨在让算法设计人员在一个统一的框架中捕获、建模和推理关于有效性和效率之间的权衡。具体地说，这个项目探索了一种新的级联检索模型，其中排名被分解为有限数量的不同阶段。每个阶段考虑的特征依次更丰富、更复杂，但考虑的候选文档集却相对较小。人们的直觉是，尽管复杂的功能计算起来更耗时，但检查较少的文档可以抵消额外的开销。换句话说，级联模型将检索视为一个多阶段渐进求精问题。基于对目前最先进的知识的调查，这是第一个探索这种方法来解决排名问题的项目，标志着与以前的“铁板一块”的排名函数有很大的不同。尽管在这一未知领域的探索存在一些风险，但这项研究有望为IR研究开辟一个新的前沿。该项目旨在通过将理论IR研究和现实世界搜索中的实际考虑结合在一起，缩小学术IR研究和行业IR研究之间的鸿沟。预计级联模型将引起网络搜索引擎公司的兴趣，从而提供一条从探索性研究成果到对生产系统产生重大影响的途径。此外，这项工作与新兴的绿色计算领域相吻合：更高效的算法使用更少的能源，从而有助于减少网络规模服务的环境足迹。项目网站(http://www.umiacs.umd.edu/~jimmylin/projects/)载有关于该项目的更多信息，并将用于发布一个原型，作为象牙开放源码检索工具包的一部分。