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KDD
2007
ACM
178views Data Mining» more  KDD 2007»
14 years 5 months ago
Real-time ranking with concept drift using expert advice
In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of comp...
Hila Becker, Marta Arias
SAC
2008
ACM
13 years 4 months ago
An efficient feature ranking measure for text categorization
A major obstacle that decreases the performance of text classifiers is the extremely high dimensionality of text data. To reduce the dimension, a number of approaches based on rou...
Songbo Tan, Yuefen Wang, Xueqi Cheng
KDD
2007
ACM
192views Data Mining» more  KDD 2007»
14 years 5 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
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
14 years 5 months ago
Query chains: learning to rank from implicit feedback
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
ADMA
2005
Springer
157views Data Mining» more  ADMA 2005»
13 years 11 months ago
Learning k-Nearest Neighbor Naive Bayes for Ranking
Accurate probability-based ranking of instances is crucial in many real-world data mining applications. KNN (k-nearest neighbor) [1] has been intensively studied as an effective c...
Liangxiao Jiang, Harry Zhang, Jiang Su