skip to main content
10.1145/1526709.1526853acmconferencesArticle/Chapter ViewAbstractPublication PageswwwConference Proceedingsconference-collections
poster

Query clustering using click-through graph

Published:20 April 2009Publication History

ABSTRACT

In this paper we describe a problem of discovering query clusters from a click-through graph of web search logs. The graph consists of a set of web search queries, a set of pages selected for the queries, and a set of directed edges that connects a query node and a page node clicked by a user for the query. The proposed method extracts all maximal bipartite cliques (bicliques) from a click-through graph and compute an equivalence set of queries (i.e., a query cluster) from the maximal bicliques. A cluster of queries is formed from the queries in a biclique. We present a scalable algorithm that enumerates all maximal bicliques from the click-through graph. We have conducted experiments on Yahoo web search queries and the result is promising.

References

  1. J. J. Carrasco, D. C. Fain, K. J. Lang, and L. Zhukov. Clustering of bipartite advertiser-keywork grdaph. Workshop on Large Scale Clustering, ICDM 2003.Google ScholarGoogle Scholar
  2. R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, Trawling the web for emerging cyber-communities. The 8th Int. World Wide Web Conference, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. K. Makino, and T. Uno, New algorithms for enumerating all maximal cliques, The 9th Scandinavian Workshop on Algorithm Theory, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  4. E. Tomita, A. Tanaka, and H. Takahashi. The worst--case time complexity for generating all maximal cliques and computational experiments. Theoretical Computer Science, 363(1), pp.28--42, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Wen, J. Nie, H. Zhang. Query clustering using user logs. ACM Transactions on Information Systems, 20(1), pp. 59--31, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Query clustering using click-through graph

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader