koostööfiltrimist

Koostööfiltrimist, also known as collaborative filtering, is a technique used by recommender systems. It aims to predict the interests of a user by collecting preferences or taste information from many users. The underlying assumption is that if person A has the same opinion as person B on an issue, A is more likely to have B's opinion on a different issue than that of a random person. This technique is widely used in e-commerce, social media, and entertainment platforms to suggest relevant items, content, or connections to users.

There are two main types of collaborative filtering: user-based and item-based. User-based collaborative filtering works by finding users who are similar to the target user and then recommending items that those similar users liked but the target user has not yet encountered. Item-based collaborative filtering, on the other hand, identifies items that are similar to items the target user has liked in the past and recommends those similar items. For example, if a user has liked a particular movie, item-based filtering might recommend other movies that users who liked the first movie also enjoyed.

Collaborative filtering systems rely on a user-item interaction matrix, where rows represent users and columns represent items. The cells in the matrix typically contain ratings or indicators of preference. By analyzing patterns in this matrix, the system can make recommendations. Despite its effectiveness, collaborative filtering can suffer from challenges such as the cold-start problem (difficulty recommending to new users or new items with no interaction data) and sparsity (most users have interacted with only a small fraction of available items).