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A state-based approach to supporting users in complex search tasks

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TitleInfo
Title
A state-based approach to supporting users in complex search tasks
Name (type = personal)
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Liu
NamePart (type = given)
Jiqun
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1992-
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Jiqun Liu
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author
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Shah
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Chirag
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Chirag Shah
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Advisory Committee
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chair
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Belkin
NamePart (type = given)
Nicholas J
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Nicholas J Belkin
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Advisory Committee
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internal member
Name (type = personal)
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Costello
NamePart (type = given)
Kaitlin L
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Kaitlin L Costello
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Advisory Committee
Role
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internal member
Name (type = personal)
NamePart (type = family)
Russell
NamePart (type = given)
Daniel M
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Daniel M Russell
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Advisory Committee
Role
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2020
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2020-05
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2020
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English
Abstract (type = abstract)
Previous work on task-based interactive information retrieval (IR) has mainly focused on what users found along the search process and the predefined, static aspects of complex search tasks (e.g., task goal, task product, cognitive task complexity), rather than how complex search tasks of different types can be better understood, examined, and disambiguated within the associated multi-round search processes. Also, it is believed that the knowledge about users' cognitive variations in task-based search process can help tailor search paths and experiences to support task completion and search satisfaction. To adaptively support users engaging in complex search tasks, it is critical to connect theoretical, descriptive frameworks of search process with computational models of interactive IR and develop personalized recommendations for users according to their task states. Based on the data collected from two laboratory user studies, in this dissertation we sought to understand the states and state transition patterns in complex search tasks of different types and predict the identified task states using Machine Learning (ML) classifiers built upon observable search behavioral features. Moreover, through running Q-learning-based simulation of adaptive search recommendations, we also explored how the state-based framework could be applied in building computational models and supporting users with timely recommendations.

Based on the results from the dissertation study, we identified four intention-based task states and six problem-help-based task states, which depict the active, planned dimension and situational, unanticipated dimension of search tasks respectively. We also found that 1) task state transition patterns as features extracted from interaction process could be useful for disambiguating different types of search tasks; 2) the implicit task states can be inferred and predicted using behavioral-feature-based ML classifiers. With respect to application, we built a search recommendation model based on Q-learning algorithm and the knowledge we learned about task states. Then we apply the model in simulating search sessions consisting of potentially useful query segments with high rewards from different users. Our results demonstrated that the simulated search episodes can improve search efficiency to varying extents in different task scenarios. However, in many task contexts, this improvement often comes with the price of hurting the diversity and fairness in information coverage.

This dissertation presents a comprehensive study on state-based approach to understanding and supporting complex search tasks: from task state and state transition pattern identification, task state prediction, all the way to the application of computational state-based model in simulating dynamic search recommendations. Our process-oriented, state-based framework can be further extended with studies in a variety of contexts (e.g., multi-session search, collaborative search, conversational search) and deeper knowledge about users' cognitive limits and search decision-making.
Subject (authority = local)
Topic
Interactive information retrieval
Subject (authority = local)
Topic
Complex search tasks
Subject (authority = local)
Topic
Task states
Subject (authority = local)
Topic
Q-learning
Subject (authority = local)
Topic
IR evaluation
Subject (authority = RUETD)
Topic
Communication, Information and Library Studies
RelatedItem (type = host)
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Title
Rutgers University Electronic Theses and Dissertations
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ETD_10781
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application/pdf
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text/xml
Extent
1 online resource (xiii, 138 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
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Identifier (type = doi)
doi:10.7282/t3-wzx6-xx50
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Liu
GivenName
Jiqun
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2020-04-20 11:05:04
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Name
Jiqun Liu
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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