Item-based Collaborative Filtering

Item-based Collaborative Filtering is a type of recommendation system that uses the similarity between items to recommend items to users. It works by analyzing the past behavior of users and their interactions with items to identify similar items. The system then recommends items that are similar to the ones that the user has already interacted with. This approach is particularly useful when there are a large number of items and users, as it can be computationally expensive to calculate the similarity between all users. Item-based Collaborative Filtering is widely used in e-commerce, social media, and content recommendation systems.
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