A Novel Approach to Recommend Products in E-Commerce

Abstract

Electronic commerce (e-commerce) refers to the purchasing and selling of products via the web. E-commerce platforms have been used by people around the globe in some form or another because everything can be purchased online with just a few mouse clicks. Since, there is a huge amount of data on every e-commerce site; a consumer may struggle to identify the product they require. In this scenario, the Recommendation System is used. A product's recommendation might be based on a variety of variables, including past search or purchase history, user reviews, and the most popular product. We have several Machine Learning-based approaches for these recommendations. We shall implement Collaborative Filtering and K-Means clustering in this work. We utilized Jupyter Notebook to develop the recommendation system, and the Amazon-ratings dataset from Kaggle was used. We will also examine numerous other recommendation techniques in this paper.