Co-clustering for queries and corresponding advertisement

Fan YANG, Bin AN, Xizhao WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

Abstract

Both documents clustering and words clustering are well studied problems. Most existing algorithms cluster documents (advertisement) and words (query) separately but not simultaneously. In this paper we present a novel idea of analyzing both queries and advertisements which occur with queries at the same time. We present an innovative co-clustering algorithm that suggests queries by co-clustering advertisements and queries. We pose the co-clustering problem as an optimization problem in information theory - the optimal co-clustering maximizes the mutual information between the clustered random variables subject to constraints on the number of row and column clusters.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Machine Learning and Cybernetics
PublisherIEEE
Pages2296-2299
Number of pages4
ISBN (Print)9781424437023
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 International Conference on Machine Learning and Cybernetics - Hebei, China
Duration: 12 Jul 200915 Jul 2009

Publication series

NameInternational Conference on Machine Learning and Cybernetics (ICMLC)
PublisherIEEE
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference2009 International Conference on Machine Learning and Cybernetics
Country/TerritoryChina
CityHebei
Period12/07/0915/07/09

Keywords

  • Co-clustering; online advertisement
  • Dbscan
  • K-mean clustering
  • Query
  • Singular value decomposition

Fingerprint

Dive into the research topics of 'Co-clustering for queries and corresponding advertisement'. Together they form a unique fingerprint.

Cite this