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  1. Proceedings of the 2009 workshop on Web Search Click Data (WSCD '09)
  2. Using query logs and click data to create improved document descriptions
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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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Using query logs and click data to create improved document descriptions

Content Provider ACM Digital Library
Author Kraaij, Wessel van der Weide, Theo van der Heijden, Maarten Hinne, Max Verberne, Suzan
Abstract Logfiles of search engines are a promising resource for data mining, since they provide raw data associated to users and web documents. In this paper we focus on the latter aspect and explore how the information in logfiles could be used to improve document descriptions. A pilot experiment demonstrated that document descriptors extracted from the queries that are associated with documents by clicks provide useful semantic information about documents in addition to document descriptors extracted from the full text of the web pages.
Starting Page 64
Ending Page 67
Page Count 4
File Format PDF
ISBN 9781605584348
DOI 10.1145/1507509.1507519
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-02-09
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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