III: EAGER: Automatically Building Test Collections Using Implicit Relevance Signals from the Web

帮助用户找到相关信息无疑是一个重要问题，对当今信息社会的运作至关重要。 因此，全世界每天都有数百万人使用搜索引擎技术也就不足为奇了。虽然现有的搜索技术运行良好，但仍有相当大的改进空间。搜索引擎创新的驱动力是快速、重复地衡量给定系统产生的结果质量的能力。 这种类型的测量通常需要某种形式的人工输入。例如，可以雇用人类专家来评估搜索结果的相关性，或者搜索引擎可以记录用户交互，诸如输入的查询和点击的结果。在收集了足够多的数据之后，它可以用来准确地衡量搜索引擎的质量。它也可以用来提高现有的搜索引擎的质量，通过一个过程被称为“调整”或“训练”。然而，收集大量的这种信息通常需要大量的人力或计算资源。因此，持续的创新是唯一可能在一个非常陡峭的cost.Techniques构建大型信息检索测试集，不需要人力的努力是本研究的主要重点。而不是依赖于人类策划的信息，隐式的相关性信号从Web挖掘自动构建大型，可重用的测试集合的各种搜索任务，包括Web搜索，新闻搜索和企业搜索。 本研究的出发点是发现网络中存在大量的隐性关联信号。隐式相关性信号的最简单示例是超链接，其可以被解释为源作者确认目标页面的相关性的信号。在这项研究中，这种隐含的相关性信号可以有效地挖掘和聚合在一个完全无监督的方式来创建测试集合，而无需任何人力的假设进行了调查。 自动生成的测试集合以两种不同的方式进行评估。首先，测试集合是根据它们与人工生成的测试集合相比准确测量搜索系统质量的能力来评估的。其次，使用自动测试集合优化的搜索引擎的质量与使用手动测试集合优化的引擎进行比较。该项目的更广泛影响来自于自动构建的测试集合，这些测试集合免费分发给更广泛的研究社区。由于在工业和学术环境中有更多的培训数据可供系统地评估和调整搜索引擎，预计搜索引擎技术将取得进展。研究生和本科生两级的研究和教育一体化以及通过各种外联方案吸引妇女和代表性不足的学生，预计会产生更广泛的影响。

Helping users find relevant information is undeniably an important problem vital to the functioning of today's information-based societies. It is therefore no surprise that millions of people worldwide make use of search engine technologies each and every day. Although existing search technologies work well, there is still considerable room for improvement. Search engine innovation is driven by the ability to rapidly, and repeatedly, measure the quality of the results produced by a given system. This type of measurement typically requires some form of human input. For example, a human expert may be hired to assess the relevance of search results, or the search engine may log user interactions, such as the queries entered and the results clicked. After a sufficiently large amount of data has been collected, it can then be used to accurately measure search engine quality. It can also be used to improve the quality of existing search engines via a process known as "tuning" or "training". However, gathering large amounts of this information typically requires a significant amount of human effort or computational resources. Therefore, sustained innovation is only possible at a very steep cost.Techniques for constructing large information retrieval test collections that require no human effort are the primary focus of this research study. Rather than relying on human-curated information, implicit relevance signals from the Web are mined to automatically construct large, reusable test collections for a variety of search tasks, including Web search, news search, and enterprise search. The observation that the Web contains a large number of implicit relevance signals is the starting point of the research. The simplest example of an implicit relevance signal is the hyperlink, which can be interpreted as a signal acknowledging the relevance of the target page by the source author. The hypothesis that such implicit relevance signals can be effectively mined and aggregated in a completely unsupervised manner to create test collections without any human effort is investigated in this research. Automatically generated test collections are evaluated in two different ways. First, the test collections are evaluated according to their ability to accurately measure the quality of search systems compared to human-generated test collections. Second, the quality of search engines tuned using the automated test collections are compared against engines tuned using manual test collections.The broader impact of this project is derived from automatically constructed test collections that are freely distributed to the broader research community. Advances in search engine technologies are expected as the result of increased availability of training data to systematically evaluate and tune search engines, both in industrial and academic settings. Additional broader impact is expected from the integration of research and education at both the graduate and undergraduate levels and from engaging women and underrepresented students through various outreach programs.