A hybrid collaborative recommender system based on user profiles

Nowadays, users are overwhelmed by the abundant amount of information delivered through the Internet. Especially in the e-commerce area, largest catalogues offer millions of products and are visited by users having a variety of interests. It is of particular interest to provide customers with personal advice: Web personalization has become an indispensable part of e-commerce. One type of personalization that many Web sites have started to embody is represented by recommender systems, which provide customers with personalized advices about products or services. Collaborative systems actually represent the state-of-the-art of recommendation engines used in most e-commerce sites. In this paper, we propose a hybrid method that aims at improving collaborative techniques by means of user profiles that store knowledge about user interests