Collaborative filtering group recommendation method based on stochastic disturbance technology

Abstract

The invention discloses a collaborative filtering group recommendation method based on a stochastic disturbance technology, mainly aims at solving the problem that a user is likely to expose private information in a collaborative filtering group recommendation process, and provides a privacy protection group recommendation method. The collaborative filtering group recommendation method based on the stochastic disturbance technology mainly includes the steps that user initial score data disturbance processing, user interest and preference values, preference action weight and frequency weight are provided, through the stochastic disturbance technology, user initial score data is processed to protect private data of users, common preference groups are formed through the user interest and preference values, group preference values are obtained through the preference action weight and the frequency weights, and on the basis of collaborative filtering of items, forecast scores of the common preference groups are obtained. The requirement is met that the groups are recommended and the private data of the users in the groups is protected at the same time.

Description

technical field [0001] The invention relates to the technical fields of privacy protection and recommendation systems, in particular to a collaborative filtering group recommendation method based on random perturbation technology. Background technique [0002] The emergence and development of Internet technology has made it possible for massive information in the network. In order to facilitate users to select the required information, people put forward the concept of recommendation system. The recommendation system is established by the recommendation service provider and realizes user preference prediction by collecting user data. The collected data may contain various private information, such as mailing address or order history, so the privacy of user preference data becomes a concern. With the development and gradual increase of network users, it is easier for users to form a group by sharing common hobbies and participate in related activities together, which stimulat...

AI Technical Summary

Problems solved by technology

[0003] Random perturbation is a method to protect privacy by adding random noise to the original data. The perturbed data generated by this method can be regarded as approximating the original data. The principle is to add perturbation noise that obeys a specific distribution to the original data. Although the fuzzy processing has changed the original data, it does not have a great impact on the results of some calculations, and the error can be widely used within a certain range.

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