A novel and optimized product recommendation method in e-commerce

The rapid growth of e-commerce has caused product overload where customers on the Web are no longer able to effectively choose the products they are exposed to. Developing an intelligent recommendation system is proposed to overcome the problem of overloaded product’s information provided by the e-commerce enterprises. This paper proposes a new hybrid recommendation scheme, called NOVEL, based on CF, WebCF-AR and WebCF-PT to enhance the recommendation quality and the system performance of current recommender systems. The NOVEL method, showing a minimal qualitative improvement of 50% compared to the present methods of product suggestion, has advanced tremendously the existing ecommerce product suggestions employing costumer navigational and behavioral algorithms.

Recommendation method ,

Collaborating filtering ,

Recommendation quality ,

Product taxonomy ,

Association rules ,