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Intelligent prediction and recommendation optimization method based on fuzzy clustering and time weighting

In view of the problem of data overload on the Internet platform, it is increasingly difficult for users to dig out useful and useful information. Many scholars have proposed various recommendation systems, but when calculating user similarity, traditional collaborative filtering recommendation algorithms often only Consider a single user scoring matrix, ignoring the impact of correlations between projects on recommendation accuracy. Therefore, an improved model of collaborative filtering recommendation is presented in this paper. Firstly, a method of item similarity measurement is introduced in the process of computing the user's nearest neighbor to get more appropriate neighbors. In addition, due to that the users interests will decay over time, time weight is added in the process of computing item ratings. Experimental results show that the proposed algorithm can obtain better performance than other traditional collaborative filtering algorithms in aspects of prediction accuracy and classification accuracy.