IMSS-E: An Intelligent Approach to Design of Adaptive Meta Search System for E Commerce Website Ranking

With the continuous increase in frequent E Commerce users, online businesses must have more customer friendly websites to better satisfy the personalized requirements of online customer and hence improve their market share over competition; Different customers have different purchase requirements at different intervals of time and hence new strategies are often required to be deployed by online retailers in order to identify the current purchase requirements of customer. In this research work, we propose design of a tool called Intelligent Meta Search System for E-commerce (IMSS-E), which can be used to blend benefits of Apriori based Map Reduce framework supported by Intelligent technologies like back propagation neural network and semantic web with B2C E-commerce to assist the online user to easily search and rank various E Commerce websites which can better satisfy his/her personalized online purchase requirement. An extensive experimental evaluation shows that IMSS-E can better satisfy the personalized search requirements of E Commerce users than conventional meta search engines.