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In this paper, we present novel methods that combine (1) Markov models and (2) Web page content search techniques to generate Web navigation recommendations. For click-stream modeling, both first-order and second-order Markov 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.