From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations

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 …

business commercial context cs.dc cs.ir cs.lg data decisions environments evolution explore impact machine machine learning power recommendation recommendations recommendation systems research role significance study systems