Building an Efficient Product Recommender System

The prodigious growth of data resulted in selling a wide variety of product categories on e-commerce websites today. To automate the process of recommending appropriate products to users, the developers introduced the recommender systems. A recommender engine recommends users products of their interest. Recommendation systems have become a vital tool for e-commerce companies, enabling them to assist their customers in finding the most suitable products, and consequently increasing their profits. In this paper, implementation of recommendation systems solely designed for products available on e-commerce applications is discussed and also a few evaluation techniques, and some challenges faced while building the system are explained. In addition to this, it is also mentioned the utility of a recommender engine that effectively relies on collaborative filtering based on singular value decomposition and building a popularity-based recommender engine using Amazon’s electronics data which is the most significant e-commerce product category. The main motive of implementing this model is to effectively suggest to users at least five top products that suit their interest and evaluate the overall performance of recommendations provided to the customers.