How Users Select Query Suggestions Under Different Satisfaction States?
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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.
KeywordsQuery suggestion User study Eye tracking User satisfaction Novelty
This work is supported in part by the Chinese National Program on Key Basic Research Project (973 Program, grant No. 2014CB744604, 2013CB329304), the Chinese 863 Program (grant No. 2015AA015403), the Natural Science Foundation of China (grant No. U1636203, 61272265, 61402324), the Tianjin Research Program of Application Foundation and Advanced Technology (grant no. 15JCQNJC41700), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 721321.1qQ.
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