Koostööfilterdamine

Koostööfilterdamine, also known as collaborative filtering, is a technique used by recommender systems. It aims to predict a user's interests and recommend items they might like based on the preferences of similar users. The core idea is that if two users have agreed on certain items in the past, they are likely to agree on other items as well.

There are two main types of collaborative filtering: user-based and item-based. User-based collaborative filtering finds users who are similar to the target user and recommends items that those similar users have 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 and recommends those similar items. Item similarity is determined by looking at user ratings or purchase histories; if many users who liked item A also liked item B, then A and B are considered similar.

Collaborative filtering has been widely adopted in various applications, including e-commerce platforms, streaming services, and social media. It is effective because it can uncover serendipitous recommendations that users might not have found otherwise. However, it faces challenges such as the cold-start problem, where it is difficult to make recommendations for new users or new items with no interaction history. Another issue is data sparsity, where user-item interaction data is often incomplete. Despite these challenges, collaborative filtering remains a fundamental and powerful tool in the field of recommender systems.