ABSTRACT
Machine learning-based product recommendation systems have become a vital component of e-commerce websites. This project aims to develop a recommendation system that utilizes machine learning algorithms to provide personalized recommendations to customers based on their browsing and purchasing history. The proposed system will analyze the customer's behavior on the website, such as their search queries, products viewed, and purchases made, to identify their preferences and predict their future interests. The system will be developed using Python and its popular machine learning libraries such as Scikit-learn, TensorFlow, and Keras. The effectiveness of the recommendation system will be evaluated through various metrics such as accuracy, precision, and recall.
Transformer-based models have revolutionized the field of natural language processing (NLP) and are widely used in various NLP tasks such as language translation, sentiment analysis, and text summarization. This project proposes the development of a transformer-based product recommendation system for e-commerce websites.
The project aims to provide a more personalized shopping experience for customers while also benefiting the e-commerce website by increasing customer engagement, retention, and sales.
