Description

Offering online adapted endorsement services helps expand customer satisfaction. Predictably, an endorsement system is considered as a success if clients purchase the recommended products. However, the act of purchasing itself does not guarantee satisfaction and a truly successful recommendation system should be one that maximizes the customer’s after-use gratification. By employing an innovative associative classification method, we are able to predict a customer’s ultimate pleasure. In our proposed system model is based on customer’s regular buying, a product will be recommended to the customer according to his/her interests in last purchase’s. We emphasize that a good recommendation system not only considers what the customer needs, but also ensures customer’s contentment. The main contributions of this research are twofold. First, we make a distinction between the customer purchase and the customer endorsement. When a customer follows advice to purchase a product we give offers in the method or cost(Concession) and service availability of the products.