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Collaborative filtering recommendation algorithm combining tag information and rating difference

Aiming at the problem of the low calculation accuracy of similarity between users in the traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm combining tag information and rating difference is proposed. Firstly, the algorithm introduces time weights to describe the dynamic change of users' interest, and defines users' preference values for tags according to tag information so as to calculate the interest similarity between users. Secondly, considering the influence of rating difference between users on recommendation effect, the algorithm uses the mean ratings as the benchmarks to calculate the rating difference between them. Finally, the algorithm comprehensively considers interest similarity and rating difference between users, generates target user's nearest neighbors and makes recommendations. The experimental results based on movieLens dataset show that the proposed algorithm effectively improves the calculation accuracy of similarity between users and the recommendation performance.