III: Medium: Learning from Implicit Feedback Through Online Experimentation

III：媒介：通过在线实验从隐式反馈中学习

• 批准号: 0905467
• 负责人: Thorsten Joachims
• 金额: $ 100万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905467&HistoricalAwards=false
• 关键词: III; Medium; Learning; Implicit; Feedback

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The goal of the project is to harness the information contained in users' interactions with information systems (e.g., query reformulations, clicks, dwell time) to train those systems to better serve their users' information needs. The key challenge lies in properly interpreting this implicit feedback and collecting it in a way that provides valid training data. Moving beyond existing passive data collection methods, the project draws on multi-armed bandit algorithms, experiment design, and machine learning to actively collect implicit feedback data. Developing these interactive experimentation methods goes hand-in-hand with developing machine learning algorithms that can use the resulting training data, and empirical evaluations that validate the models of user behavior assumed by the algorithms. This research will improve retrieval quality for important applications like intranet search and desktop search. Additionally, the project will provide an operational full-text search engine for the Physics E-Print ArXiv and potentially other digital libraries, thus forming a test-bed for the research while also providing a valuable service and dissemination tool to the academic community beyond computer science. The project provides interesting and motivating research opportunities to undergrads and international exchange students, and the PIs will include relevant material into the undergraduate and graduate curriculum. Finally, following their prior work on the Support Vector Machine, SVM-light (http://svmlight.joachims.org/) and an open-source search engine for learning ranked retrieval functions and evaluating the learned rankings, OSMOT (http://radlinski.org/osmot/), the PIs will continue to provide easy-to-use software that enables research and teaching, via the project website (http://www.cs.cornell.edu/People/tj/implicit/).

该奖项是根据2009年的《美国恢复和再投资法》（公法111-5）资助的。该项目的目的是利用用户与信息系统互动中包含的信息（例如，查询重新制定，点击，停留时间）来训练这些系统以更好地满足用户的信息需求。 关键挑战在于正确解释这种隐式反馈并以提供有效的培训数据的方式收集它。 该项目超越了现有的被动数据收集方法，它借鉴了多武器的强盗算法，实验设计和机器学习，以积极收集隐式反馈数据。 开发这些交互式实验方法与开发可以使用所得培训数据的机器学习算法以及验证算法假定的用户行为模型的经验评估并驾齐驱。 这项研究将提高Intranet搜索和桌面搜索等重要应用的检索质量。 此外，该项目将为物理E-Print ARXIV和潜在的其他数字图书馆提供操作全文搜索引擎，从而为研究构成测试床，同时还为计算机科学以外的学术社区提供了有价值的服务和传播工具。 该项目为本科和国际交流学生提供了有趣而激励的研究机会，PIS将在本科和研究生课程中包括相关材料。最后，遵循他们先前在支持矢量机SVM-Light（http://svmlight.joachims.org/）和开放式搜索引擎进行学习排名的检索功能并评估学习排名的开放式搜索引擎（http://radlinski.org/osmot/将继续），以便启用pis，以启用pis - （http://www.cs.cornell.edu/people/tj/implitic/）。