Ensemble Dynamic Machine Learning Algorithm (EDMLA) for E-commerce sentiment product recommendation system with the integration of AACSD-an empirical study

Abstract

In this paper, an attempt has been made to investigate the benefits of the Amalgamate Architecture Centric Software Development (AACSD) method through an experimental setup using Machine Learning techniques on an E-Commerce product recommender system. The system recommends products based on authorized user reviews. As part of this research, an Ensemble Dynamic Machine Learning Algorithm (EDMLA) was designed and developed with the integration of AACSD to improve performance quality. Performance was evaluated based on parameters such as sensitivity, specificity, and accuracy.