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Web usage mining is widely applied in various areas and online recommendation is one web usage mining application. WUM can model user behavior and, therefore, to forecast their future movements. In this paper, to provide online prediction efficiently, the authors advance an architecture for online predicting in web usage mining recommendation system and propose a novel approach to classifying user navigation patterns for predicting users' future requests. The approach is based on using the Longest Common Subsequence (LCS) algorithm in classification part of the system.
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