Item-based filtering

Item-based filtering is a method used by recommendation systems to suggest products or content by analyzing similarities between items. It examines user preferences and finds items that are similar based on features, user interactions, or purchase patterns. For example, if someone watches a particular movie, the system recommends other movies with similar genres, actors, or themes. This approach focuses on the relationship between items rather than users, making recommendations more efficient and often more accurate, especially when user data is limited. It’s commonly used in platforms like Netflix or e-commerce sites to personalize suggestions.