Personalized recommendation method based on probability matrix decomposition, equipment and computer readable storage medium

Abstract

The invention discloses a personalized recommendation method based on probability matrix decomposition. The personalized recommendation method comprises the steps of 1, calculating user attribute similarity; 2, calculating the attention similarity of the user to the label and the score similarity of the user to the label, and integrating the two similarities to generate the preference similarity of the user; 3, proportionally adding the user attribute similarity and the user preference similarity into 1 to generate a final user comprehensive similarity matrix; 4, initializing a user implicit feature matrix and a project implicit feature matrix into normal distribution, and fusing a user comprehensive similarity matrix into the probability matrix decomposition model; and 5, iteratively calculating a user feature matrix and a project feature matrix, generating a prediction scoring matrix of the user for the projects, and recommending the first K projects with the highest scores to the user as final recommendation results by adopting a Top-K sorting method. The method has the advantages that the user cold start problem in the recommendation system is relieved, the accuracy of the recommendation algorithm is improved, and the expandability of the algorithm is enhanced.

Description

technical field [0001] The invention belongs to the technical field of personalized recommendation, and relates to a personalized recommendation method, device and computer-readable storage medium based on probability matrix decomposition. Background technique [0002] The Probability Matrix Factorization Model PMF (Probability Matrix Factorization Recommendation) integrates the knowledge of probability theory into the traditional matrix factorization model, and predicts the user's rating of the item from the perspective of probability. The PMF model performs well on large and sparse data sets. [0003] However, there are still some shortcomings in the PMF recommendation algorithm: it does not make full use of user information and item information, resulting in low recommendation accuracy; it does not effectively alleviate the user cold start problem in the recommendation system; the scalability of the recommendation algorithm is not strong. Contents of the invention [00...

AI Technical Summary

Problems solved by technology

[0003] However, the PMF recommendation algorithm still has some shortcomings: it does not make full use of user information and item information, resulting in low recommendation accuracy; it does not effectively alleviate the user cold start problem in the recommendation system; the scalability of the recommendation algorithm is not strong

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