Comparing Features and Recommending Products Online

Now a day, it’s popular to buy a product online and online product shopping is a new trade in E-Commerce domain. Recommender systems (RS) is an essential part of the data and the e-commerce system. RS are actually created for helping purchaser, users or customer in that particular domain. Shopping websites use purchase history based, user review base recommendation technology. Every user try to buy products based on its feature configuration so our system is such very important and helpful recommendation technology which is completely depends on features of product. The customers can choose their preferred products and buy many products online from the Online shopping website or portal. Online shopping websites combine Business-to-Consumers, Business-to-business as well as Consumers-to-Consumers services with each other. Because of the customized, high feature and cost capability products the universality of the software is increases. Proposed system mainly work in two part 1) Initially our system recommend similar product using similarity algorithm and 2) Use FP growth algorithm for recommending other related products. Both work very well for our selected domain. For input feature profile we use dataset of product feature matrix and initial transaction matrix.