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    CORR
    2010
    Springer
    63views Education» more  CORR 2010»
    Learning from Logged Implicit Exploration Data
    13 years 5 months ago
    Learning from Logged Implicit Exploration Data
    Download  www.research.rutgers.edu
    Alexander L. Strehl, John Langford, Sham M. Kakade
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    CORR
    2006
    Springer
    118views Education» more  CORR 2006»
    Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs
    13 years 5 months ago
    Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs
    Download  www.cs.cornell.edu
    Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well kn...
    Filip Radlinski, Thorsten Joachims
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    NAACL
    2010
    186views Computational Linguistics» more  NAACL 2010»
    Learning Dense Models of Query Similarity from User Click Logs
    13 years 3 months ago
    Learning Dense Models of Query Similarity from User Click Logs
    Download  aclweb.org
    The goal of this work is to integrate query similarity metrics as features into a dense model that can be trained on large amounts of query log data, in order to rank query rewrit...
    Fabio De Bona, Stefan Riezler, Keith Hall, Massimi...
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    KDD
    2005
    ACM
    177views Data Mining» more  KDD 2005»
    Query chains: learning to rank from implicit feedback
    14 years 5 months ago
    Query chains: learning to rank from implicit feedback
    Download  www.cs.cornell.edu
    This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
    Filip Radlinski, Thorsten Joachims
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    KDD
    2007
    ACM
    192views Data Mining» more  KDD 2007»
    Active exploration for learning rankings from clickthrough data
    14 years 5 months ago
    Active exploration for learning rankings from clickthrough data
    Download  radlinski.org
    We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
    Filip Radlinski, Thorsten Joachims
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