What is Collaborative Filtering?

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Collaborative filtering is a technique used by recommender systems by making automatic predictions or filtering about a user’s interests. This is achieved by collecting information from many users.

Collaborative filtering is a technique used in recommender systems to make predictions or recommendations about which items a user might like, based on their preferences and behavior, as well as the preferences and behavior of similar users.

There are two main types of collaborative filtering:

  1. User-based collaborative filtering: This method recommends items by finding users who have similar preferences or behavior to the target user, and then recommending items that those similar users have liked or interacted with.
  2. Item-based collaborative filtering: Instead of finding similar users, this method identifies similar items based on the preferences or behavior of users. It recommends items that are similar to the ones that a user has already liked or interacted with.

In both cases, collaborative filtering relies on the principle of “wisdom of the crowd,” leveraging the collective behavior of users to make personalized recommendations. It does not require explicit knowledge about the items being recommended, such as their attributes or features, making it particularly useful in scenarios where item attributes are not readily available or are difficult to quantify.