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How users select query suggestions under different satisfaction states?

  • Zhenguo Shang
  • , Jingfei Li
  • , Peng Zhang*
  • , Dawei Song
  • , Benyou Wang
  • *Corresponding author for this work
  • Tianjin University
  • Open University Milton Keynes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Query suggestion (or recommendation) has become an important technique in commercial search engines (e.g., Google, Bing and Baidu) in order to improve users’ search experience. Most existing studies on query suggestion focus on formalizing various query suggestion models, while ignoring the study on investigating how users select query suggestions under different satisfaction states. Specifically, although a number of effective query suggestion models have been proposed, some basic problems have not been well investigated. For example, (i) how much the importance of query suggestion feature for users with respect to different queries; (ii) how user’s satisfaction for current search results will influence the selection of query suggestions. In this paper, we conduct extensive user study with a search engine interface in order to investigate above problems. Through the user study, we gain a series of insightful findings which may benefit for the design of future search engine and query suggestion models.

Original languageEnglish
Title of host publicationInformation Retrieval - 23rd China conference, CCIR 2017, Proceedings
EditorsJianyun Nie, Tong Ruan, Tieyun Qian, Jirong Wen, Yiqun Liu
PublisherSpringer Verlag
Pages93-105
Number of pages13
ISBN (Print)9783319686981
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event23rd China conference on Information Retrieval, CCIR 2017 - Shanghai, China
Duration: 13 Jul 201714 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10390 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd China conference on Information Retrieval, CCIR 2017
Country/TerritoryChina
CityShanghai
Period13/07/1714/07/17

Keywords

  • Eye tracking
  • Novelty
  • Query suggestion
  • User satisfaction
  • User study

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