1 Introduction
Recommender systems try to provide people with recommendations of items they will appreciate, based on their past preferences, history of purchase, and demographic information [5], [11], [13]. Three steps usually are common to the functioning of recommender systems: (1) gather valuable information on the users and on the items, (2) determine patterns from the historical data (using content-based, collaborative, or hybrid approaches as well as memory-based or model-based algorithms, see [1]), and (3) suggest items to people.