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Predicting Searcher Frustration

By Henry Feild, James Allan and Rosie Jones

Abstract

When search engine users have trouble finding information, they may become frustrated, possibly resulting in a bad experience (even if they are ultimately successful). In a user study in which participants were given difficult information seeking tasks, half of all queries submitted resulted in some degree of self-reported frustration. A third of all successful tasks involved at least one instance of frustration. By modeling searcher frustration, search engines can predict the current state of user frustration and decide when to intervene with alternative search strategies to prevent the user from becoming more frustrated, giving up, or switching to another search engine. We present several models to predict frustration using features extracted from query logs and physical sensors. We are able to predict frustration with a mean average precision of 66 % from the physical sensors, and 87% from the query log features

Topics: General Terms Experimentation, Measurement Keywords user modeling
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.182.2182
Provided by: CiteSeerX
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