Award Abstract # 1717488
III: Small: Information Fostering - Being Proactive in Information Seeking

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: RUTGERS, THE STATE UNIVERSITY
Initial Amendment Date: July 27, 2017
Latest Amendment Date: May 24, 2018
Award Number: 1717488
Award Instrument: Standard Grant
Program Manager: Maria Zemankova
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2017
End Date: February 29, 2020 (Estimated)
Total Intended Award Amount: $499,656.00
Total Awarded Amount to Date: $515,656.00
Funds Obligated to Date: FY 2017 = $58,320.00
FY 2018 = $0.00
History of Investigator:
  • Chirag Shah (Principal Investigator)
    chirags@uw.edu
Recipient Sponsored Research Office: Rutgers University New Brunswick
3 RUTGERS PLZ
NEW BRUNSWICK
NJ  US  08901-8559
(848)932-0150
Sponsor Congressional District: 12
Primary Place of Performance: Rutgers University
4 Huntington Street
New Brunswick
NJ  US  08901-8559
Primary Place of Performance
Congressional District:
12
Unique Entity Identifier (UEI): M1LVPE5GLSD9
Parent UEI:
NSF Program(s): Info Integration & Informatics
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
01001819DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7364, 7923, 9251
Program Element Code(s): 736400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 13)
Liu, Jiqun "Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors" Information Processing & Management , v.58 , 2021 https://doi.org/10.1016/j.ipm.2021.102522 Citation Details
Liu, Jiqun and Shah, Chirag "Investigating the Impacts of Expectation Disconfirmation on Web Search" Proceedings of ACM Conference on Human Information Interaction and Retrieval (CHIIR) , 2019 10.1145/3295750.3298959 Citation Details
Liu, Jiquon and Shah, Chirag "Interactive IR User Study Design, Evaluation, and Reporting" Synthesis Lectures on Information Concepts, Retrieval, and Services , v.11 , 2019 10.2200/S00923ED1V01Y201905ICR067 Citation Details
Mitsui, M. and Liu, J. and Shah, C. "How Much is Too Much? Whole Session vs. First Query Behaviors in Task Prediction." Proceedings of ACM SIGIR 2018 Conference , 2018 Citation Details
Mitsui, M. and Liu, J. and Shah, C. "How Much is Too Much? Whole Session vs. First Query Behaviors in Task Prediction." Proceedings of ACM SIGIR 2018 Conference , 2018 Citation Details
Mitsui, Matthew and Liu, Jiqun and Shah, Chirag "Coagmento: Past, Present, and Future of an Individual and Collaborative Information Seeking Platform" ACM Conference on Human Information Interaction and Retrieval (CHIIR) , 2018 10.1145/3176349.3176896 Citation Details
Mitsui, Matthew and Shah, Chirag "The Broad View of Task Type Using Path Analysis" Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) , 2018 10.1145/3234944.3234951 Citation Details
Mitsui, Matthew Shah "Bridging Gaps: Predicting User and Task Characteristics from Partial User Information" Proceedings of ACM SIGIR 2018 Conference , 2019 Citation Details
Pulliza, Jonathan and Shah, Chirag "Information Retrieval and Interaction System (IRIS): A Toolkit for Investigating Information Retrieval and Interaction Activities" ACM Conference on Human Information Interaction and Retrieval (CHIIR) , 2018 10.1145/3176349.3176895 Citation Details
Sarkar, S. and Shah, C. "An Integrated Model of Task, Information Needs, Sources and Uncertainty to Design Task-Aware Search Systems." Proceedings of ACM International Conference on Theory of Information Retrieval (ICTIR) , 2021 Citation Details
Shah, Chirag "Information Fostering - Being Proactive with Information Seeking and Retrieval: Perspective Paper" ACM Conference on Human Information Interaction and Retrieval (CHIIR) , 2018 10.1145/3176349.3176389 Citation Details
(Showing: 1 - 10 of 13)

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