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An Eye-tracking Study of User Interactions with Query Auto Completion

Katja Hofmann, Bhaskar Mitra, Filip Radlinski, and Milad Shokouhi

Abstract

Query Auto Completion (QAC) suggests possible queries to web search users from the moment they start entering a query. This popular feature of web search engines is thought to reduce physical and cognitive effort when formulating a query.

Perhaps surprisingly, despite QAC being widely used, users’ interactions with it are poorly understood. This paper begins to address this gap. We present the results of an in-depth user study of user interactions with QAC in web search. While study participants completed web search tasks, we recorded their interactions using eye-tracking and client-side logging. This allows us to provide a first look at how users interact with QAC. We specifically focus on the effects of QAC ranking, by controlling the quality of the ranking in a within-subject design.

We identify a strong position bias that is consistent across ranking conditions. Due to this strong position bias, ranking quality affects QAC usage. We also find an effect on task completion, in particular on the number of result pages visited. We show how these effects can be explained by a combination of searchers’ behavior patterns, namely monitoring or ignoring QAC, and searching for spelling support or complete queries to express a search intent. We conclude the paper with a discussion of the important implications of our findings for QAC evaluation.

Details

Publication typeInproceedings
Published inProceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM)
URLhttp://dx.doi.org/10.1145/2661829.2661922
DOI10.1145/2661829.2661922
PublisherACM – Association for Computing Machinery
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