Implicit matrix decomposition recommendation method based on differential privacy and time perception

Abstract

The invention discloses an implicit matrix factorization recommendation method based on differential privacy and time perception. The implicit matrix factorization recommendation method based on differential privacy and time perception can enable privacy protection and recommendation accuracy to be well balanced. The method comprises the following steps: firstly, normalizing score data of a user;before the score data of the user is sent to the recommendation system, distributing a privacy budget to each sub-scoring matrix; disturbing each score in the matrix; constructing a sub-matrix decomposition model by utilizing an improved implicit matrix decomposition algorithm; analyzing interests learned by the user from the data set at each time interval; obtaining a user characteristic matrix and a project characteristic matrix of a sub-matrix, mining the change trend of the characteristics of a user and a project along with time by utilizing an autoregression time sequence analysis model,calculating to obtain a behavior prediction value in a certain time period in the future, and selecting TOP-N projects with the maximum prediction score values to recommend the user.

Description

technical field [0001] The invention relates to the technical field of data security, in particular to an implicit matrix decomposition recommendation method based on differential privacy and time perception. Background technique [0002] The era of big data provides users with a wealth of information, affecting more and more people's daily life. At the same time, it has brought great opportunities and challenges to research in all walks of life. How to extract useful knowledge from massive data has become a key issue and has attracted more and more attention. The recommendation system uses data mining technology to provide users with personalized services, solves the problem of information overload, and has been applied in many industrial fields. At the same time, recommender systems have attracted the attention of many researchers. The research on recommender systems has become an important research field at present. [0003] Recommendation systems provide users with p...

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Problems solved by technology

[0004]Aiming at the problems existing in the prior art, the purpose of the present invention is to provide an implicit matrix factorization recommendation method based on differential privacy and time awareness, which protects When personal privacy data is not violated, it can not only solve the problem of user interest drift, but also alleviate data sparsity and data dimensionality reduction, and achieve a good balance between privacy protection and recommendation accuracy

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