Query Reformulation for Task-Oriented Web Searches
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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In this paper, we present novel methods that combine (1) MarkovModels and (2) web page content search techniques to generate web navigation recommendations. For clickstream modeling, both first-order and second-orderMarkov Models were studied and a compact storage format for Markov transition matrices was used. For content-based search, a search engine was used to obtain similar-content pages for recommendation to compensate for the sparsity of the Markov model and thus improve coverage. Experiments were conducted on real web clickstream logs, and confirmed the efficiency of the proposed methods.