What does collaborative filtering utilize for making recommendations?

Collaborative filtering relies heavily on user behavior and preferences to make recommendations. This approach analyzes patterns from a group of users rather than focusing on an individual user alone. By observing how users interact with different items—such as products, movies, or music—collaborative filtering identifies similarities among users based on their past interactions and preferences.

For instance, if two users have rated several items similarly, the system can recommend new items one user has enjoyed to the other user, assuming they will have similar tastes. This method harnesses community insights and effectively customizes recommendations accordingly, which is why it's a prevalent technique in many recommendation systems across various platforms.

The other options do not capture the essence of collaborative filtering. Utilizing historical data with no context would lack the necessary personalization that collaborative filtering thrives on. Random selection of content does not involve any analytical process to tailor recommendations based on user engagement or preferences. Lastly, artificial intelligence without any user data would be unable to provide meaningful recommendations, as the foundation of collaborative filtering is fundamentally built on analyzing and leveraging concrete user interactions.