Recommender Systems Questions
The main difference between item-based and user-based collaborative filtering lies in the approach used to make recommendations.
In user-based collaborative filtering, recommendations are made based on the similarity between users. The system identifies users who have similar preferences or behaviors and recommends items that those similar users have liked or rated highly. This approach assumes that users with similar tastes will have similar preferences for items.
On the other hand, item-based collaborative filtering focuses on the similarity between items. The system identifies items that are similar based on user ratings or other item attributes and recommends items that are similar to the ones a user has already liked or rated highly. This approach assumes that if a user likes one item, they are likely to enjoy similar items.
In summary, user-based collaborative filtering recommends items based on the preferences of similar users, while item-based collaborative filtering recommends items based on the similarity between items themselves.