December 14, 2017

Research Article: Predicting Searcher Frustration

Title: Predicting Searcher Frustration

Authors: Henry Feild, James Allan, Rosie Jones

Date: 2010

Source: The Center for Intelligent Information Retrieval (CIIR)
In: Proceedings of the 33rd Annual ACM SIGIR Conference (SIGIR 2010)

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 mod- eling searcher frustration, search engines can predict the cur- rent 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.

Direct to Full Text Article (8 pages; PDF)

See Also: Presentation About Article (41 Slides; PDF)

Gary Price About Gary Price

Gary Price (gprice@mediasourceinc.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at Ask.com, and is currently a contributing editor at Search Engine Land.

Share