Recommending videos watch by friends to users is call collaborative filtering. To be precise, it is call User Collaborative Filtering. 1. Overview of Collaborative Filtering (Collaborative Filtering) Collaborative filtering Ecuador Mobile Number (CF for short) is one of the most important ideas in recommender systems. In the early days, collaborative filtering was almost equivalent to recommender systems. The idea of collaborative filtering was born Ecuador Mobile Number in 1994 and was use in mail systems. In 2001, Amazon use a collaborative filtering algorithm to recommend similar products. The idea of collaborative filtering is relatively simple, there are three main types: User Collaborative Filtering (UserCF): Similar users may like the same item.
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For example, two users who have add friends or users with similar click behaviors are regard as similar users. If my brother and her wife have add Douyin friends to each other, the videos they Ecuador Mobile Numbers both like may be recommend to each other. Item Collaborative Filtering (ItemCF): Similar items may be like by the same user. This is the story of Walmart’s diapers and beer during the famous World Cup. Here, because during the World Cup, dads have to drink beer to watch the game and bring their babies. Beer and Ecuador Mobile Number diapers are need by dads at the same time, that is, similar products, which can be sold together. Model Collaborative Filtering: Use matrix factorization models to learn collaborative filtering information for users and items.
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Generally, such collaborative filtering models are: SVD, SVD++, etc. This kind of collaborative filtering is more abstract than the first two. It will not be explain here and will be describ in detail later. These algorithms Ecuador Mobile Number are explain in detail below in the order of item collaborative filtering, user collaborative filtering, and model collaborative filtering. 2. Ecuador Mobile Number Calculation of item collaborative filtering In 2003, Amazon publish a paper explaining how they use Item-to-Item Collaborative Filtering to build their “see and see” feature. As shown below: Collaborative filtering: My wife found out that she like 1,000 young ladies on Douyin Crazy.