Intelligent Wine Recommendation and Blending System using Machine Learning

Abstract - Hybrid innovative machine learning approach for a wine recommendation and blending optimization in the e-commerce industry. The proposed approach is integrated into the WineHaus platform and includes content filtering, collaborative filtering, and intelligent blending algorithms that will make personalized wine recommendations and blend suggestions. It uses Nearest Neighbors-based recommendation engines to analyze wine characteristics like taste, acidity, and alcohol content. And it will have a dynamic blending algorithm for making recommendations on the most optimal combinations of wines along with their proportions. The highly interactive platform exposes a real-time preference adjustment by two user functionalities slider and event-based selection. Key technical features are feature normalization using MinMaxScaler, efficient nearest neighbor search, ball_tree algorithm, and dynamic blend ratio optimization module. The experimental results showed highly improved outcomes concerning recommendation accuracy, precision of blend suggestions, and real-time user satisfaction. This research focuses on hybrid machine learning in e-commerce personalizations, which are directed toward increased user engagement translating into higher sales conversion rates. The intelligent wine recommendation and blending system is a unique approach for the entire setting of product personalization for the future e-commerce industry.