DocumentCode
498796
Title
Co-clustering for queries and corresponding advertisement
Author
Yang, Fan ; An, Bin ; Wang, Xizhao
Author_Institution
Dept. of Comput. Sci., Univ. of California, Santa Cruz, CA, USA
Volume
4
fYear
2009
fDate
12-15 July 2009
Firstpage
2296
Lastpage
2299
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.
Keywords
advertising; query processing; advertisement; coclustering; documents clustering; optimization problem; queries; words clustering; Advertising; Clustering algorithms; Computer science; Cybernetics; Intrusion detection; Machine learning; Machine learning algorithms; Mathematics; Random variables; Search engines; Co-Clustering; DBSCAN; K-mean clustering; Online advertisement; Query; Singular Value Decomposition;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location
Baoding
Print_ISBN
978-1-4244-3702-3
Electronic_ISBN
978-1-4244-3703-0
Type
conf
DOI
10.1109/ICMLC.2009.5212131
Filename
5212131
Link To Document