Product Recommendation Systems For Online Platforms

The exponential growth of e-commerce has led to an overwhelming abundance of products, making it challenging for consumers to find items that align with their preferences. Product recommendation systems have emerged as essential tools to enhance user experience by providing personalized suggestions. This paper delves into various recommendation methodologies, including collaborative filtering, content-based filtering, hybrid approaches, and deep learning techniques. It also explores the challenges faced in implementing these systems, such as scalability, cold-start problems, and data sparsity. Furthermore, the paper discusses evaluation metrics and real-world applications, providing insights into the effectiveness of different recommendation strategies.