yhteistyösuodattimet

Yhteistyösuodattimet, or collaborative filtering in English, is a recommendation system technique used to make predictions about the interests of a user by collecting preferences from many users. The underlying assumption is that if person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person.

There are two main types of collaborative filtering. User-based collaborative filtering finds users with similar preferences to the target user and recommends items that those similar users have liked. Item-based collaborative filtering, on the other hand, finds items similar to those the target user has liked and recommends those similar items. Similarity between users or items is typically calculated using measures like cosine similarity or Pearson correlation based on their past ratings or interactions.

Collaborative filtering has been widely adopted by e-commerce platforms, streaming services, and social media to provide personalized recommendations. For example, Netflix uses it to suggest movies and TV shows, and Amazon uses it to recommend products.

Despite its effectiveness, collaborative filtering faces challenges. The "cold-start problem" occurs when new users or items are introduced, as there is not enough data to make accurate recommendations. Sparsity, where user-item interaction data is very limited, can also degrade performance. Additionally, the system can suffer from "popularity bias," where popular items are recommended more frequently, potentially overshadowing niche items.