EAGER: Learning to Efficiently Rank with Cascades

不可否认的是，文本搜索对于当今以信息为基础的社会至关重要，它帮助用户在网页、期刊文章、新闻故事、博客、电子邮件、tweet和无数其他来源中定位相关信息。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，它几乎只关注有效性，而往往忽略了排序函数的运行速度（即效率）。该项目有助于学习有效排名的新兴研究领域，其目的是让算法设计者在统一的框架中捕获，建模和推理有效性和效率之间的权衡。具体来说，该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。每个阶段依次考虑更丰富和更复杂的特征，但依次考虑更小的候选文档集。直观的感觉是，尽管复杂特性的计算更耗时，但检查更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的细化问题。基于对当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“单一”排名功能的实质性背离。虽然在这个未知领域的探索有一定的风险，但这项研究有望为红外研究开辟一个新的前沿。该项目旨在通过将理论红外研究与“现实世界”搜索中的实际考虑结合起来，缩小学术和工业红外研究之间的鸿沟。预计级联模型将引起网络搜索引擎公司的兴趣，从而为从探索性研究结果到生产系统的重大影响提供一条路径。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，因此有助于减少网络规模服务的环境足迹。该项目的网站（http://www.umiacs.umd.edu/~jimmylin/projects/）包含有关该项目的更多信息，并将用于发布原型，作为Ivory开源检索工具包的一部分。

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.