Enhancing Recommendation on Customizable E-Commerce System with Association&Ranking

The propose system is a web recommender that models user habits and behaviors by constructing a knowledge base using temporal web access patterns as input. The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products which is exposed to. The proposed system introduces a personalized recommendation procedure by which it can get further recommendation effectiveness when applied to Internet shopping malls with respect to the financial status of the customer. The suggested procedure is based on Web usage mining of relationship between the customer and product as well as his income status and from the most sold out product using association rule mining and Fuzzy C Means. Fuzzy logic is applied to represent real-life temporal concepts and requested resources of periodic pattern-based web access activities.