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» Learning to cluster web search results
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ICDE
2007
IEEE
146views Database» more  ICDE 2007»
14 years 9 months ago
Challenges on Distributed Web Retrieval
In the ocean of Web data, Web search engines are the primary way to access content. As the data is on the order of petabytes, current search engines are very large centralized sys...
Ricardo A. Baeza-Yates, Carlos Castillo, Flavio Ju...
CIKM
2010
Springer
13 years 6 months ago
Exploiting site-level information to improve web search
Ranking Web search results has long evolved beyond simple bag-of-words retrieval models. Modern search engines routinely employ machine learning ranking that relies on exogenous r...
Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josif...
ICML
2009
IEEE
14 years 2 months ago
Fast evolutionary maximum margin clustering
The maximum margin clustering approach is a recently proposed extension of the concept of support vector machines to the clustering problem. Briefly stated, it aims at finding a...
Fabian Gieseke, Tapio Pahikkala, Oliver Kramer
IPM
2007
156views more  IPM 2007»
13 years 7 months ago
p2pDating: Real life inspired semantic overlay networks for Web search
We consider a network of autonomous peers forming a logically global but physically distributed search engine, where every peer has its own local collection generated by independe...
Josiane Xavier Parreira, Sebastian Michel, Gerhard...
WWW
2005
ACM
14 years 8 months ago
Three-level caching for efficient query processing in large Web search engines
Large web search engines have to answer thousands of queries per second with interactive response times. Due to the sizes of the data sets involved, often in the range of multiple...
Xiaohui Long, Torsten Suel