An efficient and optimized recommendation system using social network knowledge base

With the advent of e-commerce in the current market a large number of companies e.g. Flipkart, eBay, infibeam, amazon etc. have come up with a huge range of products on a single platform to the users. These products are recommended to the users based on certain parameters related to the user. Moreover, in order to refine the recommendation these e-commerce based websites have started using social networking sites to access information pertaining to the user in order to improve their recommendation. In this paper we present a novel recommendation system for e-commerce websites using social network knowledge base that uses certain parameters provided by users viz. age group, gender, location etc. and based on these criteria best recommendation is provided by our proposed method using Analytical Hierarchy Process, Merge-and-Sort, and Sort-and-Count algorithms within a wrapper to optimize. User preferences are taken from Facebook whereas Flipkart is chosen as the e-commerce website for illustration of our proposed method.