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Personalized Web page recommendation is strongly limited by the nature of web logs, the intrinsic complexity of the problem and the tight efficiency requirements. When tackled by traditional Web usage mining techniques, due to the presence of an huge number of meaningful clusters and profiles for visitors of a typical highly rated Website, the model-based or distance-based methods tend to make too strong and simplistic assumptions or, conversely, to become excessively complex and slow. In this paper, a heuristic Â¿majority intelligenceÂ¿ strategy is designed, that easily adapts to changing navigational patterns, without the costly need to explicitly individuate them before navigation. The proposed approach mimics human behavior in an unknown environment in presence of many individuals acting in parallel and is able to predict with good accuracy and in real time the next page category visited by a user. The method has been tested on real data coming from users who visited a popular Website of generic content. Average accuracy on test sets is good on a 17 class problem and, most remarkably, it remains stable as the Web navigation goes on.