Collaborative filtering is a type of recommendation system that predicts a user's preferences or interests by leveraging similarities between users or items. This method is based on the idea that users who have liked similar items in the past are likely to have similar preferences in the future. Collaborative filtering can be implemented using different approaches, such as user-based filtering, item-based filtering, or matrix factorization. This technique is commonly used in e-commerce websites, social media platforms, and streaming services to personalize recommendations for users.