Rating detection by reviews using ML and NLP towards mobile phone recommendation

Abstract

Product recommendation is a type of marketing tool that has become increasingly important for businesses as well as in purchasing goods in the digital age. Prod uct recommendation is the process of suggesting items to customers based on their previous purchases or choices and is a form of personalization where the goal is to provide relevant, valuable, and timely information to customers to help them make decisions about what to buy. The purpose of product recommendations is to in crease customer engagement, loyalty, and ultimately, sales while ensuring customers help buying products according to their preference. By providing customers with personalized product recommendations, businesses are able to increase customer satisfaction and loyalty, as well as drive sales. On the other way, customers also feel secure while purchasing products according to their personality and choices. This paper builds a product recommendation system by analyzing the techniques of Machine Learning and Natural Language Processing. The focus of the research is on recommending mobile phone products to users based on their preferences and interests. The system was advanced and examined using a dataset of mobile phone specifications and user reviews. The study’s findings demonstrate that the sug gested recommendation system may offer users accurate and pertinent ideas; but, due to dataset restrictions, the system cannot be expanded to include other kinds of products. However, the proposed system can be used for taking personalized require ments and finding a better result for them with improved accuracy and precision which ultimately will enhance customer satisfaction.