Neural networks are increasingly used in recommendation systems to provide personalized content or product suggestions. These systems rely on collaborative filtering, content-based filtering, or hybrid methods to recommend items based on user preferences or item characteristics. Neural networks, particularly deep learning models, can enhance these systems by learning complex patterns in user behavior or item features.
For example, in collaborative filtering, neural networks can model user-item interactions to make predictions about which items a user may like based on similar users' preferences. In content-based filtering, CNNs can be used to analyze images or text descriptions of items to recommend similar items.
Neural networks allow recommendation systems to scale and improve as they learn from vast amounts of user data, enhancing accuracy and personalization, particularly in e-commerce, music, movie, and content platforms.