A novel approach for providing accurate services recommendation for big data analytics in e-commerce

Abstract

Today the world is an overcrowded so that the product recommendations are required for recommending products or services. Purchasing a false-positive product may reduce customer’s reliability over the website and this reduces the usage of the site. Day by day, the amount of customers, products and information has grown rapidly that is the problem so need scalability and efficiency when processing or analysis of this data on a large scale. If we can utilize social media data that is provide a huge pool of data about users for the highly accurate service recommendation but privacy issues of users and finally the customer is choosing irrelevant product may be shown with a higher rank in recommendation list. To avoid these problems, a novel recommendation system using a collaborative filtering algorithm is implemented in Apache Hadoop for Big Data. The intelligent recommender system is used to interface between a customer’s request and the recommender system for highly accurate services of the product recommendation.