A hybird recommendation system with vector profile based on fuzzy clustering

Abstract

To cope with the problems of information overloading and low signal to noise ratio, a number of potential solutions based on information filtering or web mining have been proposed. In this paper, we present a hybrid information filtering mechanism into the development of a recommendation system for distance learning. The web pages and users’ logs are represented into vector profiles first. The resulting profiles, then, are classified into peer-group based on fuzzy clustering. The similarity among profiles is characterized as a membership function allowing system to provide recommendation flexibly. Case-based reasoning is also incorporated into the system to support the controlling of browsing order in e-Learning. Besides, in order to catch the rapid changes of users’ interests, a corresponding feedback mechanism is also included. Finally, we have also implemented a web site, On-Line Learner (OLLer), and set up several experiments to evaluate the effectiveness of our system.