A product recommendation system suggests items to users based on their preferences, behavior, and context. These systems analyze data such as browsing history, purchase patterns, and ratings to predict what users are likely to find interesting or useful.
Common approaches include collaborative filtering, which identifies patterns among similar users, and content-based filtering, which suggests items with similar attributes to those the user has interacted with. Hybrid systems combine both methods for better accuracy.
Applications include e-commerce, where personalized recommendations drive sales, and streaming platforms, where they enhance user engagement by suggesting relevant content.