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Han, Shuguang (2018) UNDERSTANDING, MODELING AND SUPPORTING CROSS-DEVICE WEB SEARCH. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Recent studies have witnessed an increasing popularity of cross-device web search, in which users resume their previously-started search tasks from one device to later sessions on another. This novel search mode brings new user behaviors such as cross-device information transfer; however, they are rarely studied in recent research. Existing studies on this topic mainly focused on automatic cross-device search task extraction and/or task continuation prediction; whereas it lacks sufficient understanding of user behaviors and ways of supporting cross-device search tasks. Building an automated search support system requires proper models that can quantify user behaviors in the whole cross-device search process. This motivates me to focus on understanding, modeling and supporting cross-device search processes in this dissertation.

To understand the cross-device search process, I examine the main cross-device search topics, the major triggers, the information transfer approaches, and users’ behavioral patterns within each device and across multiple devices. These are obtained through an on-line survey and a lab-controlled user study with fine-grained user behavior logs. Then, I work on two quantitative models to automatically capture users' behavioral patterns. Both models assume that user behaviors are driven by hidden factors, and the identified behavioral patterns are either the hidden factors or a reflection of hidden factors. Following prior studies, I consider two types of hidden factors --- search tactic (e.g., the tactic of information re-finding/finding would drive to click/skip previously-accessed documents) and user knowledge (e.g., knowing the knowledge within a document would drive users to skip the document). Finally, to create a real-world cross-device search support use case, I design two supporting functions: one to assist information re-finding and the other to support information finding. The effectiveness of different support functions are further examined through both off-line and on-line experiments.

The dissertation has several contributions. First, this is the first comprehensive investigation of cross-device web search behaviors. Second, two novel computational models are proposed to automatically quantify cross-device search processes, which are rarely studied in existing researches. Third, I identify two important cross-device search support tasks and implement effective algorithms to support both of them, which can beneficial future studies for this topic.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Han, Shuguanghanshuguang@gmail.comshh69
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHe, Daqingdah44@pitt.edudah44
Brusilovsky, Peterpeterb@pitt.edupeterb
Lin, Yuruyurulin@pitt.eduyurulin
Date: 24 January 2018
Date Type: Publication
Defense Date: 14 April 2017
Approval Date: 24 January 2018
Submission Date: 5 December 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 180
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Cross-device Web Search Cross-Device Search Process Cross-device Search Support Search Process Modeling Information Re-finding Information Exploration Search Tactics Search as Knowledge Learning
Date Deposited: 24 Jan 2018 16:27
Last Modified: 24 Jan 2018 16:27


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