Integration of Data Science and Artificial Intelligence for Effective Online Supply Chain Recommendation System

The purpose of the research is to tackle the cold-start problem in supply chain recommendation systems by integrating Data Science and Artificial Intelligence. The objective is to improve system accuracy, minimize bounce rates, and offer effective recommendations to new or cold users, nurturing continued engagement in online e-commerce transactions. The research has carried out the Hydride Technique of “Content-Based Association Clustering” to overwhelmed cold-start issues in recommender classifications. This practice has integrated clustering and connotation rule tactics. The study has explored several models addressing cold-start difficulties. It has highlighted the cutting edge of recommender systems, and forecasts upcoming software applications. Taxation has involved a recommendation architecture and applied qualitative valuation method, while the anticipated “Hydride Method of Content-Based Association Clustering” architecture. It has established the efficiency in justifying sparsity challenges, reducing bounce rates, advancing employer profiles, and providing precise recommendations for new users in online e-commerce transactions. The investigate outcomes have revealed that the anticipated “Hydride Method of Content-Based Association Clustering” architecture, integrating clustering and association rule approaches, successfully overwhelms cold-start challenges in recommender classifications. It has addressed sparsity problems, minimized bounce rates, and improved user engagement in online e-commerce transactions representative near-perfect correctness. It has donated to improved e-commerce experiences by minimizing bounce rates, enlightening user engagement, and providing precise recommendations for new or cold users.