Matrix decomposition recommendation method based on joint clustering
A recommendation method, matrix decomposition technology, applied in the direction of instruments, complex mathematical operations, calculations, etc., can solve the problem of low time efficiency of collaborative filtering algorithm
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[0105] In order to verify the effect of the method in this patent, the operating environment of the experiment is first set up: Intel Core i5CPU, 3.00GHZ main frequency, Windows10 system, 12G memory. This article selects the MovieLens 10M data set commonly used in recommendation systems. For each tag in the data set, it is deleted if there are less than 5 different users and movies; for each different user and movie, less than 5 Different tags are also removed.
[0106] In this paper, the root mean square error (RMSE) is used as the evaluation criterion.
[0107] This paper selects four methods to compare the effects with the methods proposed in this paper, namely Probability Matrix Factorization (PMF), Label-based Probability Matrix Factorization (NHPMF), Joint Clustering Algorithm (Co-Clustering) and Co-Clustering+ There are four types of PMF. Specifically, according to the experimental results, the results can be drawn as shown in Table 1:
[0108] Table 1 RMSE values ...
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