Content-based Filtering
Content-based filtering is a technique used in recommender systems that recommends items to users based on their past preferences and interests. It works by analyzing the attributes of items that a user has liked or interacted with in the past and then recommending similar items that share those attributes. In other words, it focuses on the content of the items rather than the behavior of other users. Content-based filtering is commonly used in e-commerce, music, and video streaming platforms to provide personalized recommendations to users. One of the advantages of content-based filtering is that it can provide recommendations for new or niche items that have not been rated by other users yet.
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