University of Illinois Urbana-Champaign

Intent modeling and automatic query reformulation for search engine systems

Duan, Huizhong

Content Files
Huizhong_Duan.pdf

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https://hdl.handle.net/2142/46834

Description

Title
Intent modeling and automatic query reformulation for search engine systems
Author(s)
Duan, Huizhong
Issue Date
2014-01-16T18:17:49Z
Director of Research (if dissertation) or Advisor (if thesis)
Zhai, ChengXiang
Doctoral Committee Chair(s)
Zhai, ChengXiang
Committee Member(s)
  • Han, Jiawei
  • Roth, Dan
  • Kiciman, Emre
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
2014-01-16T18:17:49Z
Keyword(s)
  • search intent
  • query reformulation
Abstract
Understanding and modeling users' intent in search queries is an important topic in studying search engine systems. Good understanding of search intent is required in order to achieve better search accuracy and better user experience. In this thesis work, I identify and study three major problems in the subject: ambiguous search intent, ineffective query formulation and vague relevance criteria. To systematically study these problems, the thesis consists of three parts. In the first part, I study search intent ambiguity in search engine queries and propose a click pattern-based method that captures ambiguous search intent based on behavioral difference rather than semantic difference. Analysis shows that the proposed method is more accurate and robust in measuring query ambiguity. In the second part, I study how to provide query formulation support to facilitate users in expressing search intent. Query completion and correction, and syntactic query reformulation are proposed and studied in this part. Experiments show that the proposed query formulation support methods can help users formulate more effective queries and alleviate search difficulty. In the third part, I study how to model search intent so that we can gain insights about users' behaviors and leverage the knowledge to improve search engines. Two topics are studied in this part: modeling search intent with data level representation and discovering coordinated shopping intent in product search. It is shown that the proposed methods can not only discover meaningful user intent but also improve search and other related applications. The proposed models and algorithms in the thesis are general and can be applied to improve search accuracy in potentially many different search engines. As a systematic study on intent modeling and automatic query reformulation in search engine systems, this thesis work also provides a road map to future exploration on intent understanding and analysis.
Graduation Semester
2013-12
Permalink
http://hdl.handle.net/2142/46834
Copyright and License Information
Copyright 2013 Huizhong Duan

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