Mining clickthrough data for collaborative web search

ABSTRACT

This paper is to investigate the group behavior patterns of search activities based on Web search history data, i.e., clickthrough data, to boost search performance. We propose a Collaborative Web Search (CWS) framework based on the probabilistic modeling of the co-occurrence relationship among the heterogeneous web objects: users, queries, and Web pages. The CWS framework consists of two steps: (1) a cube-clustering approach is put forward to estimate the semantic cluster structures of the Web objects; (2) Web search activities are conducted by leveraging the probabilistic relations among the estimated cluster structures. Experiments on a real-world clickthrough data set validate the effectiveness of our CWS approach.

REFERENCES

• 1: J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI, pages 43--52. Morgan Kaufman, 1998.
• 2: Inderjit S. Dhillon , Subramanyam Mallela , Dharmendra S. Modha, Information-theoretic co-clustering, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956764]
• 3: B. Smyth, E. Balfe, O. Boydell, K. Bradley, P. Briggs, M. Coyle, and J. Freyne. A live-user evaluation of collaborative web search. In IJCAI, pages 1419--1424, 2005.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.5 On-line Information Services
          Subjects: Web-based services

General Terms:
Algorithms, Experimentation, Performance

Keywords:
clickthrough data, collaborative web search, cube-clustering