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This paper describes a surfer model which incorporates information about topic continuity derived from the surfer's history. Therefore, unlike earlier models, it captures the interrelationship between categorization (context) and ranking of Web documents simultaneously. The model is mathematically formulated. A scalable and convergent iterative procedure is provided for its implementation. Its different characteristic features, as obtained from the joint probability matrix, and their significance in Web intelligence are mentioned. Experiments performed on Web pages obtained from WebBase confirm the superiority of the model.
Web intelligence, probabilistic surfer history, page ranking, stochastic processes, context identification, categorization.
B.L. Narayan, Sankar K. Pal, Soumitra Dutta, "A Web Surfer Model Incorporating Topic Continuity", IEEE Transactions on Knowledge & Data Engineering, vol. 17, no. , pp. 726-729, May 2005, doi:10.1109/TKDE.2005.69
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