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  1. Proceedings of the 2009 workshop on Web Search Click Data (WSCD '09)
  2. Intentional query suggestion: making user goals more explicit during search
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Survey and evaluation of query intent detection methods
Analysis of long queries in a large scale search log
Distinguishing humans from robots in web search logs: preliminary results using query rates and intervals
Incremental learning to rank with partially-labeled data
Generating unambiguous URL clusters from web search
Topic-specific analysis of search queries
Optimising topical query decomposition
Search shortcuts using click-through data
Query suggestions using query-flow graphs
Using query logs and click data to create improved document descriptions
Intentional query suggestion: making user goals more explicit during search
Usefulness of quality click-through data for training
Comparative analysis of clicks and judgments for IR evaluation
Tailoring click models to user goals

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Intentional query suggestion: making user goals more explicit during search

Content Provider ACM Digital Library
Author Strohmaier, Markus Kröll, Mark Körner, Christian
Abstract The degree to which users' make their search intent explicit can be assumed to represent an upper bound on the level of service that search engines can provide. In a departure from traditional query expansion mechanisms, we introduce Intentional Query Suggestion as a novel idea that is attempting to make users' intent more explicit during search. In this paper, we present a prototypical algorithm for Intentional Query Suggestion and we discuss corresponding data from comparative experiments with traditional query suggestion mechanisms. Our preliminary results indicate that intentional query suggestions 1) diversify search result sets (i.e. it reduces result set overlap) and 2) have the potential to yield higher click-through rates than traditional query suggestions.
Starting Page 68
Ending Page 74
Page Count 7
File Format PDF
ISBN 9781605584348
DOI 10.1145/1507509.1507520
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-02-09
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Query suggestion User intent
Content Type Text
Resource Type Article
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