III: Small: Characterizing and Evaluating Whole Session Interactive Information Retrieval

III：小：描述和评估整个会话交互式信息检索

• 批准号: 1423239
• 负责人: Nicholas Belkin
• 金额: $ 49.94万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423239&HistoricalAwards=false
• 关键词: III; Small; Characterizing; Evaluating; Whole

This research addresses a newly important issue in contemporary life. As people become more accustomed to using the Web for finding information, they are increasingly using it for addressing ever more complex and personally important information problems. However, current Web search engines have been developed and specifically tuned to helping people find simple, mostly factual information, usually as a single response list to a single, simple query. But when they try to address the new types of problems, people need to engage in longer information seeking episodes than the one query-one response paradigm assumes. They may also need to engage in many activities other than just clicking on a search result, such as reading, evaluating, comparing and using information. Current Web search engines do not sufficiently support this model of information seeking and use. This research addresses this problem by studying why people engage in such complex information seeking (that is, the reasons that motivate them to do this), and what they try to accomplish during the course of an information seeking episode (their search intentions). The end-goal of this research is to design and evaluate new types of search engines for supporting people in accomplishing the goals that have led them to engage in information seeking. This means, in essence, being able to personalize system support to the individual, and the individual's goals and context. Specifically, this research will establish relationships between people's behaviors during an information seeking episode, the motivating goals that led them to engage in information seeking, and their specific intentions at any point in an information seeking episode. This will enable development of systems that will be able to predict how best to support the individual person in addressing their information problem. For example, the findings from this project could help build a system that automatically identifies that a searcher is shopping for a car, and help him/her compare cost-benefits of new vs. used cars, buying vs. leasing, and eventually making an informed decision. Research will be integrated with educational activities via developing modules to supplement courses in iSchools and library/information science programs, etc. This is important, since a broad range of students would learn about new methods of searching and related user studies and evaluation. Little is known about the relationships between observable searcher behaviors and the higher-level intentions which a searcher wishes to accomplish at any particular point during an information seeking episode, nor are there good methods or measures for evaluating interactive information retrieval system support for whole information seeking episode evaluation, or for evaluation of support for any particular searcher intention during an information seeking episode. This project addresses the problems of: recognizing searcher intentions during the course of an information seeking episode through observation of searcher behavior; developing methods and measures for evaluation of interactive information retrieval system support for those intentions during the course of an information seeking episode; and, developing measures and methods for evaluating the performance of the interactive information retrieval system in support of the entire information seeking episode. The observed behaviors include, e.g., eye-fixations, mouse movements, clicking, following links, page transitions and query reformulations. The project proceeds in three stages. First, it builds on and extends the results of previous and current research in relating observable searcher behaviors to high-level searcher intentions, with the goal of characterizing and segmenting information seeking episodes according to these behaviors and their associated intentions, and to develop models of information seeking episode behaviors. This is conducted both with previously collected data, and on data derived from a new user studies. Stage 2 collects data on "real" information seeking episodes, through a browser plug-in logging tool, and interviews with participants. These data are analyzed to discover new motivating tasks, to identify new information seeking episode intentions, and to enhance the models of information seeking episode behaviors. Stage 3, including a further study of people conducting complex information seeking tasks, identifies the goals of intentions during the information seeking episode and for the whole information seeking episode, and develops and tests measures of support for those goals, and methods for gathering the data required for applying the measures. Stage 3 also investigates the application of the behavior models for simulation of information seeking episodes. In essence, this project will result in statistical models and algorithms that predict a searcher's intentions, leading to better ways for personalization and recommendations. These outcomes will be novel and significant because they will allow us to address goal/task accomplishment, and not just search improvement, through a user's information seeking episode(s). The results from this project will be disseminated through the project website (http://inforetrieval.org/iir/). The website will specifically include technical reports, publications, and links to datasets and open-source software resulted from this project.

这项研究解决了当代生活中一个新的重要问题。随着人们越来越习惯于使用Web来查找信息，他们越来越多地使用它来解决更加复杂和个人重要的信息问题。然而，目前的Web搜索引擎已经被开发出来，并专门用于帮助人们找到简单的、大多数是事实的信息，通常作为对单个简单查询的单个响应列表。但是，当他们试图解决新类型的问题时，人们需要投入更长的时间来寻找信息，而不是一个查询-一个响应范式所假设的那样。他们可能还需要参与许多活动，而不仅仅是点击搜索结果，比如阅读、评估、比较和使用信息。当前的Web搜索引擎不能充分支持这种信息查找和使用模式。本研究通过研究为什么人们参与如此复杂的信息搜索（即，激励他们这样做的原因），以及他们在信息搜索过程中试图完成的目标（他们的搜索意图）来解决这个问题。本研究的最终目标是设计和评估新型搜索引擎，以支持人们完成导致他们从事信息搜索的目标。这意味着，从本质上讲，能够个性化系统对个人的支持，以及个人的目标和环境。具体而言，本研究将建立人们在信息寻求事件中的行为、导致他们参与信息寻求的激励目标以及他们在信息寻求事件中任何时刻的特定意图之间的关系。这将使系统的开发能够预测如何最好地支持个人解决他们的信息问题。例如，这个项目的发现可以帮助建立一个系统，自动识别搜索者正在购买汽车，并帮助他/她比较新车与二手车的成本效益，购买与租赁，并最终做出明智的决定。研究将通过开发模块与教育活动相结合，以补充学校和图书馆/信息科学课程等的课程。这很重要，因为广泛的学生将学习新的搜索方法和相关的用户研究和评价。对于可观察到的检索者行为与检索者在信息检索事件中任何特定点希望完成的更高层次意图之间的关系，我们知之甚少，也没有很好的方法或措施来评估交互式信息检索系统对整个信息检索事件评估的支持，或评估对信息检索事件中任何特定检索者意图的支持。本项目主要解决以下问题：通过观察搜索者的行为来识别搜索者在信息搜索过程中的意图；制定方法和措施，以评估交互式信息检索系统在信息检索过程中对这些意图的支持；在此基础上，研究了交互式信息检索系统在整个信息检索过程中的性能评价措施和方法。观察到的行为包括，例如，眼睛注视，鼠标移动，点击，跟随链接，页面转换和查询重新表述。该项目分三个阶段进行。首先，它建立并扩展了以往和当前将可观察搜索者行为与高层次搜索者意图联系起来的研究成果，目的是根据这些行为及其相关意图来表征和分割信息搜索事件，并建立信息搜索事件行为模型。这是根据先前收集的数据和从新用户研究中获得的数据进行的。第二阶段通过浏览器插件登录工具和参与者访谈，收集“真实”信息搜索事件的数据。对这些数据进行分析，以发现新的激励任务，识别新的信息寻找情节意图，并改进信息寻找情节行为模型。第3阶段，包括对执行复杂信息寻找任务的人进行进一步研究，确定信息寻找阶段和整个信息寻找阶段的意图目标，并制定和测试支持这些目标的措施，以及收集应用这些措施所需数据的方法。第三阶段还研究了行为模型在信息寻找事件模拟中的应用。从本质上讲，这个项目将产生预测搜索者意图的统计模型和算法，从而为个性化和推荐提供更好的方法。这些结果将是新颖和重要的，因为它们将允许我们通过用户的信息搜索事件来解决目标/任务的完成，而不仅仅是搜索的改进。该项目的结果将通过项目网站（http://inforetrieval.org/iir/）发布。该网站将特别包括技术报告、出版物、数据集链接以及该项目产生的开源软件。