Title:
Design of an hybrid Recommender system: Personalization, Evaluation
and Prediction
Abstract: In this paper, we develop an hybrid recommender
system that combines two recommendation paradigms: collaborative recommendation
and content-based recommendation. In this recommender system, we use two
main techniques of Data Mining. First, we apply associations mining [Agrawal
et al (1994)] to customer purchase data in order to derive relationships
between product classes and subclasses. Second, we use clustering [Michaud
(1997)] to assign customers into groups with similar interests, based on
their prior purchase patterns. The proposed system suggests new products
to a customer and predicts his preference for each product contained in his
personalized list of recommendation. These predictions are computed using
evaluation data provided by each customer concerning his previous purchased
products.