The knowledge extracted from the analysis of historical information of a web server can be used to develop personalization or recommendation systems. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web Site. Typically these systems are composed by two parts. One, executed offline, that analyze the server access logs in order to find a suitable categorization, and another executed online which is aimed at classifying the active requests, according to the previous offline analysis. In this paper we propose a WUM recommendation system, implemented as a module of the Apache web server, that is able to dynamically generate suggestions to pages that have not yet been visited by a user and might be of his potential interest. Differently from previously proposed WUM systems, SUGGEST 2.0 incrementally builds and maintain the historical information, without the need for an offline component, by means of a novel incremental graph partitioning algorithm. In the last part, we also analyze the quality of the suggestions generated and the performance of the module implemented. To this purpose we introduce also a new quality metric which try to estimate the effectiveness of a recommendation system as the capacity of anticipating users' requests that will be made farther in the future¹.