Collaborative filtering is a technique used by recommender systems to filter information by using the interactions and data collected by the system from other users. It is based on the idea that people who agreed in their evaluation of certain items are likely to agree again in the future. Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions about the interests of a user by collecting preferences or taste information from many users. The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have Bs opinion on a different issue than that of a randomly chosen person. Collaborative filtering encompasses techniques for matching people with similar interests and making recommendations on this basis. Collaborative filtering algorithms often require users active participation, an easy way to represent users interests, and algorithms that are able to match people with similar interests.

Collaborative filtering uses similarities between users and items simultaneously to provide recommendations. This allows for serendipitous recommendations; that is, collaborative filtering models can recommend an item to user A based on the interests of a similar user B. There are two classes of Collaborative Filtering: User-based, which measures the similarity between target users and other users, and Item-based, which measures the similarity between the items that target users rate or interact with and other items. Collaborative filtering algorithms can be used to recommend all kinds of items, such as products, movies, songs, articles, and more.

Collaborative filtering algorithms typically require large data sets and have been applied to many different kinds of data including sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors. Collaborative filtering is the most common technique used when it comes to building intelligent recommender systems that can learn to give better recommendations as more information about users is collected.