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139
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SIGIR
2011
ACM
14 years 3 months ago
Pseudo test collections for learning web search ranking functions
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...
110
Voted
AAAI
2006
15 years 2 months ago
Comparative Experiments on Sentiment Classification for Online Product Reviews
Evaluating text fragments for positive and negative subjective expressions and their strength can be important in applications such as single- or multi- document summarization, do...
Hang Cui, Vibhu O. Mittal, Mayur Datar
KDD
2007
ACM
192views Data Mining» more  KDD 2007»
16 years 1 months ago
Active exploration for learning rankings from clickthrough data
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
113
Voted
CIKM
2009
Springer
15 years 7 months ago
A general magnitude-preserving boosting algorithm for search ranking
Traditional boosting algorithms for the ranking problems usually employ the pairwise approach and convert the document rating preference into a binary-value label, like RankBoost....
Chenguang Zhu, Weizhu Chen, Zeyuan Allen Zhu, Gang...
81
Voted
KDD
2002
ACM
197views Data Mining» more  KDD 2002»
16 years 1 months ago
SimRank: a measure of structural-context similarity
The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents...
Glen Jeh, Jennifer Widom