NDLI: Beyond clicks: query reformulation as a predictor of search satisfaction

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Beyond clicks: query reformulation as a predictor of search satisfaction

Content Provider	ACM Digital Library
Author	Hassan, Ahmed Craswell, Nick Shi, Xiaolin Ramsey, Bill
Abstract	To understand whether a user is satisfied with the current search results, implicit behavior is a useful data source, with clicks being the best-known implicit signal. However, it is possible for a non-clicking user to be satisfied and a clicking user to be dissatisfied. Here we study additional implicit signals based on the relationship between the user's current query and the next query, such as their textual similarity and the inter-query time. Using a large unlabeled dataset, a labeled dataset of queries and a labeled dataset of user tasks, we analyze the relationship between these signals. We identify an easily-implemented rule that indicates dissatisfaction: that a similar query issued within a time interval that is short enough (such as five minutes) implies dissatisfaction. By incorporating additional query-based features in the model, we show that a query-based model (with no click information) can indicate satisfaction more accurately than click-based models. The best model uses both query and click features. In addition, by comparing query sequences in successful tasks and unsuccessful tasks, we observe that search success is an incremental process for successful tasks with multiple queries.
Starting Page	2019
Ending Page	2028
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505682
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query reformulation Success prediction Re-querying behavior Search tasks
Content Type	Text
Resource Type	Article

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