Implementation of Knowledge based Collaborative Filtering and Machine Learning for E-Commerce Recommendation System

This is the era of I-way. The development of high-speed computing and huge storage devices change the working culture of human. It affects the traditional business processes and shifted towards online business. It creates huge problems like overload and irrelevant information which are the causes of confusion both customers as well as enterprise. Recommendation system solves these problems. Design and development of efficient system is one of the key areas of the recent researchers. Collaborative filtering (CF) and content-based filtering algorithms are widely used in the implementation of such system. Collaborative used user’s features while content-based used item’s features. Most of the CFs are rating or review based processed homogeneous information. In this paper we proposed knowledge-based collaborative filtering algorithm for large data set that uses various activities done by users during interaction of item through E-commerce web site like clicks, select and purchase. The performance of the system is compared with the base models using real time Amazon E-commerce dataset using precession, recall and NDCG evaluation parameters in various combinations of activities performed by users on items.