Product Recommendations

Abstract

Product recommendations, or PRs, are becoming more and more popular every day. Product recommendations are an e-commerce customization strategy that creates products for a customer on a webpage, application, or email on a continual basis based on information such user attributes, browsing habits, or situational context, creating a personalized shopping experience. The recommendation system predicts or suggests a product based on the customer's preferences. Several e-commerce websites have adopted product suggestion systems in the modern day. A website that enables anyone to purchase and sell digital goods, services, and tangible items without needing to visit a physical store. An e-commerce website allows a business to handle customer service, shipping and logistics, payments, and orders. A recommendation may be of any kind. As an example, Spotify may be used to propose music, and movies YouTube for videos, Netflix, the Play Store (for several categories), and so on. To suggest products based on user preferences, a variety of filtering techniques and algorithms were employed. Machine Learning Techniques (MLT) that were currently in use for product recommendation were covered in this study. With these methods, an algorithm is utilized to forecast or suggest comparable things based on the user's input.