An online recommender system

One of the most important class of Data Mining applications is the so called 'Web Mining Systems' which analyzes and extracts important and non-trivial knowledge from Web related data. Typical applications of Web Mining are represented by the personalization or recommender systems. These systems are aimed to extract knowledge from the analysis of historical information of a web server in order to improve the web site expressiveness in terms of readability and content availability. Typically, these systems are made up of two parts. One, which is usually executed off-line, analyzes the server access logs in order to find a suitable categorization, and another, which is usually executed online, classifies the active requests, according to the previous off-line analysis. In this paper we propose SUGGEST 2.0 a recommender system which differently from previously proposed WUM systems does not make use of an off-line component. Moreover, in the last part of the paper, we analyze the quality of the generated suggestions and the performance of our solution. To this purpose we also introduce a new quality metric which try to estimate the effectiveness of a recommender system as the capacity to anticipate users' requests that will be issued farther in the future.