An Online Recommender System for Large Web Sites

In this paper we propose a WUM recommender system, called SUGGEST 3.0, that dynamically generates links to pages that have not yet been visited by a user and might be of his potential interest. Differently from the recommender systems proposed so far, SUGGEST 3.0 does not make use of any off-line component, and is able to manage Web sites made up of pages dynamically generated. To this purpose SUGGEST 3.0 incrementally builds and maintains historical information by means of an incremental graph partitioning algorithm, requiring no off-line component. The main innovation proposed here is a novel strategy that can be used to manage large Web sites. Experiments, conducted in order to evaluate SUGGEST 3.0 performance, demonstrated that our system is able to anticipate users´ requests that will be made farther in the future, introducing a limited overhead on the Web server activity.