III: Small: Information Fostering - Being Proactive in Information Seeking

三：小：信息培育——主动寻求信息

• 批准号: 1717488
• 负责人: Chirag Shah
• 金额: $ 49.97万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717488&HistoricalAwards=false
• 关键词: III; Small; Information; Fostering; Being

People often have difficulty in expressing their information needs. Many times this results from a lack of clarity regarding the task at hand, or the way an information or search system works. In addition, people may not know what they do not know. The former is addressed by search systems by providing recommendations, whereas there are no good solutions for the latter problem. Even when a search system makes recommendations, they are limited to suggesting objects such as queries and documents only. They do not consider providing suggestions for strategies, people, or processes. This project will address such problems by investigating the nature of the work a person is doing, predicting the potential problems they may encounter, and providing help to overcome those problems. Such a help could be an object such as a document or a query, a strategy, or a person. This whole process is referred to as Information Fostering. Beyond creating a general-purpose recommender system, Information Fostering is an idea for providing proactive suggestions and help to information seekers. This could allow them to avoid potential problems and capture promising opportunities in search before it is too late. In order to meet these goals, the project will carry out three lab studies. Through these studies, a new system will be created. This system will be integrated in a user's Web browser to provide real-time assessment of the information seeking process, as well as recommendations for queries, documents, strategies, and people. The outcomes of this project will make it possible and easier for a user with even low information literacy to be able to leverage the power of information. Such users may use information for multiple life contexts, including healthcare (e.g. caring for a sick family member), financial well-being (e.g., deciding on an investment portfolio), work (e.g., reviewing a business proposition), and education (e.g., compiling a report).Current systems face challenges in understanding the problems that information seekers face due to their inability to express their information needs, recognizing a potential problem during a search episode, and identifying support needed that goes beyond what a typical search system could provide. Most recommender systems try to mitigate these problems by suggesting information objects (queries, documents), disregarding a deeper understanding of the task at hand or the possibility of recommendations that involve process/strategy, people, and other forms. The project will advance our understanding of these information-seeking problems at the task level, and of when and how help could be offered to information seekers. The offered help would go beyond recommending alternative queries and documents and would include recommending search strategies. This will be done in three phases with different user studies: (1) extracting the nature of task, problems, and help to build Task Model and Problem-Help Model; (2) testing the validity of Task Model and Problem-Help Model in being able to detect tasks, problems, and help; and (3) creating an Information Fostering system and evaluating its effectiveness in various search tasks. There will be three major intellectual outcomes: (1) Task Model that detects the nature of a search task using implicit signals such as browsing behaviors; (2) Problem-Help Model that uses behavioral data and other contextual factors, including the nature of the task, to explicate possible problems and potential solutions without explicitly asking from the searchers; and (3) a general-purpose recommender system framework, called Information Fostering, that proactively creates recommendations in real time for enhancing one's information seeking process and helping one avoid potential problems or grab an opportunity before it is too late. The results from this project will be disseminated through the project website, which will include technical reports, publications, and links to datasets and open-source software developed in this project.

人们往往难以表达他们的信息需求。很多时候，这是由于对手头的任务或信息或搜索系统的工作方式缺乏明确性。此外，人们可能不知道他们不知道的事情。搜索系统通过提供推荐来解决前一个问题，而对于后一个问题没有很好的解决方案。即使当搜索系统进行推荐时，它们也仅限于推荐诸如查询和文档之类的对象。他们不考虑为战略、人员或流程提供建议。该项目将通过调查一个人正在做的工作的性质，预测他们可能遇到的潜在问题，并提供帮助来解决这些问题。这样的帮助可以是一个对象，如文档或查询、策略或人。这整个过程被称为信息培养。除了创建一个通用的推荐系统之外，信息培育是一个为信息搜索者提供积极建议和帮助的想法。这可以让他们避免潜在的问题，并在为时已晚之前抓住有希望的搜索机会。为了实现这些目标，该项目将进行三项实验室研究。通过这些研究，将建立一个新的系统。该系统将与用户的网络浏览器相结合，对信息搜索过程进行实时评估，并就查询、文件、战略和人员提出建议。该项目的成果将使信息素养较低的用户能够更容易地利用信息的力量。这样的用户可以使用用于多个生活背景的信息，包括医疗保健（例如，照顾生病的家庭成员）、财务健康（例如，决定投资组合），工作（例如，审查商业提议），和教育（例如，当前的系统在理解信息寻求者由于他们无法表达其信息需求而面临的问题、识别搜索事件期间的潜在问题以及识别超出典型搜索系统所能提供的所需支持方面面临挑战。大多数推荐系统试图通过建议信息对象（查询，文档）来缓解这些问题，而不考虑对手头任务的更深入理解或涉及过程/策略，人员和其他形式的建议的可能性。该项目将促进我们对这些信息寻求问题的理解，以及何时和如何向信息寻求者提供帮助。所提供的帮助将不仅仅是推荐替代查询和文件，还将包括推荐搜索策略。本研究将分三个阶段进行：（1）提取任务、问题和帮助的性质，建立任务模型和问题-帮助模型;（2）测试任务模型和问题-帮助模型在检测任务、问题和帮助方面的有效性;（3）创建信息培养系统，并评估其在各种搜索任务中的有效性。主要的智力成果有三个：（1）任务模型，它使用诸如浏览行为等隐含信号来检测搜索任务的性质;（2）问题-帮助模型，它使用行为数据和其他上下文因素，包括任务的性质，来解释可能的问题和潜在的解决方案，而不需要搜索者明确地询问;以及（3）一个通用的推荐系统框架，称为信息培育，其主动地在真实的时间内创建推荐，以增强人们的信息寻求过程，并帮助人们避免潜在的问题或在为时已晚之前抓住机会。该项目的成果将通过项目网站传播，其中将包括技术报告、出版物以及与该项目开发的数据集和开放源码软件的链接。

项目成果

The Broad View of Task Type Using Path Analysis

使用路径分析的任务类型的广泛视图

• DOI: 10.1145/3234944.3234951
• 发表时间: 2018
• 期刊: Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR
• 作者: Mitsui, Matthew;Shah, Chirag
• 通讯作者: Shah, Chirag

Coagmento: Past, Present, and Future of an Individual and Collaborative Information Seeking Platform

Coagmento：个人和协作信息搜索平台的过去、现在和未来

• DOI: 10.1145/3176349.3176896
• 发表时间: 2018
• 期刊: ACM Conference on Human Information Interaction and Retrieval (CHIIR
• 作者: Mitsui, Matthew;Liu, Jiqun;Shah, Chirag
• 通讯作者: Shah, Chirag

Interactive IR User Study Design, Evaluation, and Reporting

• DOI: 10.2200/s00923ed1v01y201905icr067
• 发表时间: 2019-06
• 期刊: Synthesis Lectures on Information Concepts, Retrieval, and Services
• 作者: Jiqun Liu;C. Shah

Coagmento v3.0: Rapid Prototyping of Web Search Experiments

• DOI: 10.1145/3295750.3298917
• 发表时间: 2019-03
• 期刊: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval
• 作者: Diana Soltani;M. Mitsui;C. Shah

Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors

• DOI: 10.1016/j.ipm.2021.102522
• 发表时间: 2021
• 期刊: Inf. Process. Manag.
• 作者: Jiqun Liu