The rapid growth of e-commerce platforms has significantly increased the number of products available to online consumers. As a result, customers often face difficulties in discovering products that match their preferences and requirements. Recommendation systems have emerged as an effective solution for addressing this challenge by providing personalized product suggestions based on user behavior and interests. This research presents the design and development of an Online E-Commerce Recommendation System that analyzes user activities such as browsing history, search patterns, product views, and purchase history to generate relevant product recommendations. The proposed system utilizes web technologies along with recommendation algorithms to enhance product discovery and improve user experience. The recommendation engine assists customers in finding suitable products efficiently, thereby reducing search time and increasing customer satisfaction. From a business perspective, the system contributes to higher customer engagement, improved conversion rates, and increased sales performance. The study demonstrates how recommendation systems can play a crucial role in modern e-commerce applications and highlights their potential for future integration with Artificial Intelligence and Machine Learning techniques.
E-Commerce, Recommendation System, Product Recommendation, Machine Learning, Customer Behavior Analysis, Personalized Shopping.
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