From Data to Decisions: The Power of Machine Learning in Business Recommendations - Kapilya Gangadharan; Anoop Purandaran; K. Malathi; Barathi Subramanian; Rathinaraja Jeyaraj; Soon Ki Jung (IEEE Access)
This research aims to explore the impact of machine learning (ML) on the evolution and efficacy of recommendation systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of recommendation engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These REs not only streamline information discovery and enhance collaboration, but also accelerate knowledge acquisition, which is vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with the individual needs of the customer. The research identifies the growing expectations of users for a seamless and intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research includes exploring advances in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and using ML in RS for researchers and practitioners to tap into the full potential of personalized recommendation in commercial business prospects.