Optimizing product recommendation model in E-commerce

Abstract

The amount of information in the world is increasing far more quickly than our ability to process it. Now it is time to create technologies that can help us sift through all the available information to find what is most valuable to us. One solution to this information overload problem is the use of product recommendation systems. Product Recommendation Systems are used by e-Commerce sites to suggest products to their customers and to provide consumers with information to help them determine which products to purchase. The products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This research tries to give a complete history of recommender methods such as CF, WRFM, WebCF-AR and some hybrid approaches, and introduces their advantages and their drawbacks. After introducing the product recommendation methods, this research introduces a novel and new method known as "INORM" that enjoys the advantages of various currently available methods.