CAREER: Improving Information Access by Learning from User Interactions

该项目采用机器学习方法来提高信息访问工具的有效性，特别是搜索引擎的检索质量。学习能力使搜索引擎能够自动调整其检索策略，以适应个别用户、特定用户组和特定的WWW站点。例如，搜索引擎应该知道，在c.s.cornell.edu网站上搜索“迈克尔·乔丹”的用户比普通用户更有可能指向加州大学伯克利分校的教授。类似地，搜索引擎应该能够适应集合属性，例如，在特定的内部网中，不是TITLE字段，而是H1标题包含最重要的信息。由于明确的用户反馈很少可用，从可观察到的用户行为中获得的隐式反馈被用作学习算法的输入。这种隐式反馈需要新的机器学习方法，因为它的形式不同于标准的机器学习设置。例如，在搜索引擎中，利用点击数据作为成对偏好形式的反馈更为合理。“对于查询Q，文档A的排名应该高于文档B”)，而不是作为绝对相关性反馈。本项目分析隐式点击数据的可靠性，设计和分析学习方法，并评估其在教育数据库上的适用性，为科学界提供服务。除了这种直接贡献之外，这项技术还可以用于提高b谷歌等通用搜索引擎的性能，因此在科学界之外具有更广泛的影响。关于这个项目的信息可以在网站http://www.cs.cornell.edu/People/tj/career上找到。生成的软件将提供下载。

The project takes a machine learning approach to improving the effectiveness of information access tools, in particular the retrieval quality of search engines. The ability to learn enables a search engine to automatically adapt its retrieval strategy to individual users, to specific user groups, and to particular WWW sites. A search engine should learn, for example, that a query for ``Michael Jordan'' issued from a user at cs.cornell.edu is much more likely to refer to the professor at UC Berkeley than for an average user. Similarly, a search engine should be able to adapt to collection properties, for example, that in a particular intranet not the TITLE field, but the H1 headlines contain the most important information.Since explicit user feedback is rarely available, implicit feedback derived from observable user behavior is used as the input to the learning algorithms. Such implicit feedback requires new machine learning methods, since it comes in forms that are different from the standard machine learning settings. For examples, in search engines it is more reasonable to exploit clickthrough data as feedback in the form of pair-wise preferences (e.g. ``for query Q, document A should be ranked higher than document B'') than as an absolute relevance feedback. The project analyzes the reliability of implicit clickthrough data, designs and analyzes learning methods, and evaluates their applicability on an educational database, providing a service to the scientific community. Beyond this direct contribution, this technology can be used to improve the performance of general purpose search engines such as Google and hence has broader impacts beyond the scientific community. Information on this project is available on the web http://www.cs.cornell.edu/People/tj/career. The resulting software will be made available for download.