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User-based collaborative filtering algorithm
4.0
The core idea is to calculate the similarity between the target user and other users (or items) according to the scored data of the users in the system, and then sort them according to the similarity, and select the users (or items) with the highest similarity to form the nearest neighbor set. Finally, the nearest neighbor set is formed according to the similarity set According to the score of users (or items) in the system, we can predict the items that the target users may be interested in, and then actively generate the recommendation list.
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K-means clustering algorithm
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Input: number of clusters K, user preference matrix ur
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