Authors: Assistant Professor Mrs. Priyanka Bamne, Nimesh Agrawal
Abstract: The increasing volume of products on online platforms has made product recommendation systems (PRS) essential for enhancing user experience and driving sales. This survey paper provides a comprehensive review of PRS, focusing on their necessity, implementation methods, and relevance in e-commerce and digital marketplaces. We explore the motivation behind recommendation systems, emphasizing their role in improving customer satisfaction, personalization, and business profitability. Various implementation techniques, including collaborative filtering, content-based filtering, hybrid filtering, and deep learning methods, are analyzed with a discussion on their advantages and limitations. Furthermore, we examine real-world applications, challenges such as cold start and scalability, and emerging trends in AI-driven recommendations. To establish the relevance of these concepts, we review key research papers, industry applications, and case studies from platforms like Amazon, Netflix, and Spotify. Finally, we highlight future directions, including explainable AI, privacy-aware recommendations, and real-time personalization, offering insights for researchers and practitioners aiming to enhance recommender systems.
