Abstract
As websites are increasing day by day, so user behavior analysis for improving the website performance attracts many researcher. This paper introduces the web page recommendation model using web log feature of web mining. Here work has introduce Feed forward counter model (FFC) for identifying the association rule with single data iteration technique. Hence execution time for this gets reduced. Work has introduced the Particle swarm optimization algorithm for the selection of appropriate page from given user path as recommendation page. This work involves support of the association rule as fitness value. Experiment was done on real dataset obtained from project tunnel website. Results shows that by the use of Feed forward association rule with PSO for next page recommendation system has improve various evaluation parameters like precision, coverage, m-metric.
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