OSE: Optimizing User Segmentation in E-Commerce Using APRIORI Algorithm for Personalized Product Recommendations

E-commerce platforms has led to an overwhelming increase in product choices, making it more challenging for users to find relevant products. To tackle this issue, a robust ECommerce Product Recommendation System is crucial. This system utilizes machine learning techniques, specifically user segmentation and the Apriori algorithm, to personalize recommendations based on user behavior, preferences, and historical data. The research aims to enhance the personalization of the shopping experience, improving user satisfaction, increasing sales, and fostering customer retention. The project focuses on developing a personalized recommendation system that combines Collaborative Filtering (both user-based and itembased) and the Apriori algorithm for association rule mining to recommend products based on frequent itemsets. The system will be trained on an anonymized e-commerce dataset containing user interactions such as purchase history, ratings, and product metadata. Key performance metrics like Precision, Recall, and F1Score will be used to evaluate the recommendation system's accuracy and effectiveness. The system will be designed to provide real-time recommendations tailored to user preferences, enabling e-commerce platforms to increase customer engagement and drive sales. Additionally, it will incorporate strategies to address coldstart problems (new users and products) and will adapt to evolving user preferences over time, ensuring that the recommendations remain dynamic and highly personalized.