• 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