NDLI: Predicting query difficulty on the web by learning visual clues

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Predicting query difficulty on the web by learning visual clues

Content Provider	ACM Digital Library
Author	Beitzel, Steven M. Frieder, Ophir Jensen, Eric C. Chowdhury, Abdur Grossman, David
Abstract	We describe a method for predicting query difficulty in a precision-oriented web search task. Our approach uses visual features from retrieved surrogate document representations (titles, snippets, etc.) to predict retrieval effectiveness for a query. By training a supervised machine learning algorithm with manually evaluated queries, visual clues indicative of relevance are discovered. We show that this approach has a moderate correlation of 0.57 with precision at 10 scores from manual relevance judgments of the top ten documents retrieved by ten web search engines over 896 queries. Our findings indicate that difficulty predictors which have been successful in recall-oriented ad-hoc search, such as clarity metrics, are not nearly as correlated with engine performance in precision-oriented tasks such as this, yielding a maximum correlation of 0.3. Additionally, relying only on visual clues avoids the need for collection statistics that are required by these prior approaches. This enables our approach to be employed in environments where these statistics are unavailable or costly to retrieve, such as metasearch.
Starting Page	615
Ending Page	616
Page Count	2
File Format	PDF
ISBN	1595930345
DOI	10.1145/1076034.1076155
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-15
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query difficulty Web search
Content Type	Text
Resource Type	Article

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