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Author Dan, Ovidiu ♦ Davison, Brian D.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Abandonment ♦ Advanced users ♦ Browser plugin logs ♦ Browser toolbar logs ♦ Novice users ♦ Predicting satisfaction ♦ Query difficulty ♦ Query logs ♦ Query performance ♦ Satisfaction ♦ Search engine evaluation ♦ Search engine switching prediction ♦ Search sessions ♦ Search success ♦ Search tasks ♦ Task completion ♦ Task difficulty ♦ User behavior models ♦ User dissatisfaction ♦ User frustration ♦ User satisfaction ♦ Web search success
Abstract Search satisfaction is defined as the fulfillment of a user’s information need. Characterizing and predicting the satisfaction of search engine users is vital for improving ranking models, increasing user retention rates, and growing market share. This article provides an overview of the research areas related to user satisfaction. First, we show that whenever users choose to defect from one search engine to another they do so mostly due to dissatisfaction with the search results. We also describe several search engine switching prediction methods, which could help search engines retain more users. Second, we discuss research on the difference between good and bad abandonment, which shows that in approximately 30% of all abandoned searches the users are in fact satisfied with the results. Third, we catalog techniques to determine queries and groups of queries that are underperforming in terms of user satisfaction. This can help improve search engines by developing specialized rankers for these query patterns. Fourth, we detail how task difficulty affects user behavior and how task difficulty can be predicted. Fifth, we characterize satisfaction and we compare major satisfaction prediction algorithms.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-07-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 49
Issue Number 1
Page Count 35
Starting Page 1
Ending Page 35


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Source: ACM Digital Library