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
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;
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
DOI :
10.1109/ICMLC.2009.5212131