Accurate User Modeling of Search and Decision Making Tasks for Improved Offline Evaluation of Information Retrieval

我的研究项目的目标是让搜索信息变得更安全、更有效。信息检索，俗称搜索，随着网络搜索引擎(如Google)和通过搜索引擎提供信息访问的组织的无处不在的使用而流行。工程师和研究人员使用测试收集和线下有效性测量来提高搜索结果的质量。离线评估允许工程师以负担得起的价格和高效地模拟他们的算法变化的影响，并衡量这些变化对搜索用户的帮助或损害的程度。我的研究计划致力于通过更准确地对用户行为进行建模，来提高离线有效性测量的预测能力。近期目标包括1)创建新的方法来有效地构建支持基于用户模型的有效性度量的测试集合，以及2)通过对单个查询和搜索结果对之外的用户交互进行建模来提高有效性度量的预测精度。在接下来的5年及以后，我的研究计划将侧重于根据用户的任务结果来评估搜索的研究，而不仅仅是用户检索的文档。例如，许多搜索用户求助于网络搜索，以帮助他们做出与健康相关的问题的决定。不幸的是，用户可能会认为包含错误信息的文档与他们的决策任务相关。我们已经证明，偏向不正确信息的搜索结果会显著降低搜索者的决策准确性。其他研究人员表明，主要的搜索引擎是有偏见的，对于与健康相关的搜索，用户可能只有45%-52%的时间会看到正确的答案。当搜索引擎引导人们做出关于他们的医疗保健的错误决定时，不仅金钱可能被浪费在诈骗治疗上，而且人们的健康可能会受到损害。我的研究计划专注于衡量搜索引擎的效率，以便研究人员和工程师有正确的衡量标准来指导他们的工作。我已经提出了一项工作，研究使用搜索引擎进行决策，并预测人们在使用搜索引擎进行决策支持时所做决策的准确性。我们最初的研究领域将是与健康相关的搜索。作为我研究计划的一部分，我参与组织了TREC决策跟踪，它为研究人员提供了一个提高搜索引擎质量、减少人们接触医疗错误信息和改进决策的场所。这项拟议的研究可以帮助每个使用搜索引擎的人避免错误信息并做出更好的决定，从而为他们带来改善。该研究项目还为学生提供高价值领域的培训，如数据分析、用户建模、机器学习和大规模数据处理。

My research program's objective is to make the search for information safer and more effective. Information retrieval, commonly known as search, is prevalent today with the ubiquitous use of web search engines (e.g. Google) and organizations providing information access via search engines. Engineers and researchers use test collections and offline effectiveness measures to improve the quality of search results. Offline evaluation allows engineers to affordably and efficiently simulate the effect of their algorithmic changes and measure the extent to which these changes help or harm search users. My research program works to improve the predictive ability of offline effectiveness measures by more accurately modeling user behaviour. Near-term objectives include 1) creating new methods to efficiently construct test collections that support user-model-based effectiveness measures and 2) improved prediction accuracy for effectiveness measures by modeling user interaction beyond a single query and search results pair. In the next 5 years and beyond, my research program will move to emphasize research on the evaluation of search in terms of the task outcome for a user rather than simply the documents retrieved by the user. For example, many search users turn to web search to help them make decisions about health-related issues. Unfortunately, users can perceive documents containing incorrect information as relevant to their decision-making tasks. We have shown that search results biased towards incorrect information can significantly reduce searchers' decision accuracy. Other researchers have shown that major search engines are biased and users may only view correct answers 45-52% of the time for health related searches. When search engines lead people to incorrect decisions about their health care, not only can money be wasted on scam treatments, but people's health can be harmed. My research program focuses on measuring search engine effectiveness so that researchers and engineers have the correct measurements to guide their work. I have proposed a line of work to study decision-making with search engines and to predict the accuracy of the decisions people reach when they use search engines for decision support. Our initial domain of study will be health-related search. The TREC Decision Track, which I co-organize as a part of my research program, provides a venue for researchers to improve the quality of search engines and reduce people's exposure to medical misinformation and improve their decisions. This proposed research can lead to improvements for everyone who uses search engines by helping them avoid misinformation and make better decisions. The research program also provides training for students in high value areas such as data analytics, user modeling, machine learning, and large scale data processing.