University of Illinois Urbana-Champaign

Data-driven assistance for user decision making on mobile devices

Liu, Xueqing

Content Files
LIU-DISSERTATION-2019.pdf

Permalink

https://hdl.handle.net/2142/106259

Description

Title
Data-driven assistance for user decision making on mobile devices
Author(s)
Liu, Xueqing
Issue Date
2019-12-06
Director of Research (if dissertation) or Advisor (if thesis)
  • Zhai, ChengXiang
  • Xie, Tao
Doctoral Committee Chair(s)
  • Zhai, ChengXiang
  • Xie, Tao
Committee Member(s)
  • Gunter, Carl
  • Enck, William
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2020-03-02T21:58:28Z
Keyword(s)
  • Decision making
  • Mobile security
  • Multi-faceted navigation
  • Natural language to database
Abstract
Mobile devices are ubiquitous. As of 2019, two-thirds of the world population own a mobile phone. Mobile devices are indispensable for supporting billions of users' information access activities such as searching, browsing news, and shopping. Among those activities, users may often need to make decisions when the mobile device is the only available channel for their information access. However, users' mobile decision-making experience is hindered by the physical characteristics of mobile devices: they are small and it is difficult to type on these devices. Furthermore, both editing and navigation would be harder than that on computers. These characteristics result in more difficulties for users to search, digest and compare information, which are the necessary steps in the process of decision making. Can we make it very easy for users to make decisions on mobile devices? In this dissertation, for the first time, we investigate the techniques for improving users' mobile decision making experience as a whole. We identify that the key to assisting user decision making is through suggesting external knowledge to bridge their knowledge gap. To this end, we propose to learn or mine such external knowledge from massive mobile-related data. We investigate three important real-world decision-making problems on mobile devices: mobile shopping decisions (Chapter 2), security decisions (Chapter 2 and Chapter 4), and business decisions (Chapter 5). We bridge users' knowledge gap in the following ways. In the first problem, we leverage a search-engine log to expand the missing information in user queries (Chapter 2); in the second problem, we leverage the Google Playstore meta-data to retrieve explanatory information to directly address users' confusion (Chapter 3 and Chapter 4), finally, in the third problem, we leverage text-to-SQL data to generate SQL from a natural language question, so that users can easily query the database using natural language (Chapter 5). Our experimental results prove that massive mobile-related data can be leveraged to effectively assist users' mobile decision making by suggesting external knowledge.
Graduation Semester
2019-12
Type of Resource
text
Permalink
http://hdl.handle.net/2142/106259
Copyright and License Information
Copyright 2019 Xueqing Liu

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois
Dissertations and Theses - Computer Science
Dissertations and Theses from the Siebel School of Computer Science