How collaborative filtering recommends from ratings alone

Collaborative filtering in recommender systems uses patterns of user ratings to suggest items. It works by finding similar users or items and predicting ratings based on their patterns. To avoid bias, users are mean-centered before comparison. Item-item filtering, where similar items are found based on user ratings, has proven to be effective in practice. This approach is used by Amazon in its 'customers who bought this also bought that' feature.