Implementation of Collaborative Filtering for Product Recommendation in E-Commerce to Enhance Scalability and Performance

Abstract

With the advancement of technology and the Internet facilities, e-commerce business is growing very fast. E-commerce is trending across geographical boundaries. A user can place order sitting at home, and the product is delivered to the given address within the specified time period. At the same time, user is given a variety of options to choose from, thus making e-commerce a much convenient way of shopping. E-commerce applications nowadays have millions of users worldwide and billions of products to offer. Thus, it becomes difficult for a user to find a product of their choice from millions of available choices. Recommendation plays an important role in e-commerce applications. A recent study on the concerned topic has advocated for a recommendation system which can be a potential solution to the mentioned problem. This system uses various parameters such as users’ purchase history, products in the cart, users’ ratings and review to recommend an item to the target user. A good recommendation system is one which helps the user to find the appropriate product and also helps the organization to grow vertically as well as horizontally. Further, collaborative filtering is an algorithm which may be applied to product recommendation which successfully satisfies customer’s needs and at the same time also helps in organization’s growth. One of the biggest challenges these days is to provide efficiency and scalability while handling a large number of users and products. The research work studies and discusses different approaches of implementation of collaborative filtering for product recommendation system. The study also shows how to overcome the drawbacks of user-based collaborative filtering by implementing item-based collaborative filtering. This research work also demonstrates how collaborative filtering can be implemented for big data using Hadoop, to overcome scalability problem and enhance performance.