Collaborative filtering recommendation method based on trustful privacy maintenance in personalized search

Abstract

The invention relates to a collaborative filtering recommendation method based on trustful privacy maintenance in personalized search and belongs to the field of computer networks. According to the basic idea, firstly, the trust degree Tcf of a target user to the execution behavior of a collaborative filtering recommendation system is established by means of automatic trust negotiation between the target user and the collaborative filtering recommendation system; the target user judges the degree of disclosure of personal information according to the trust degree Tcf, the collaborative filtering recommendation system collects the personal information provided by the target user and conducts clustering on users to obtain an initial user set with the interest similar to that of the target user; finally, the target user selects a similar user with the trust degree not lower than the Tcf from the user set and makes the similar user as a recommended object. The collaborative filtering recommendation method based on trustful privacy maintenance can overcome the defect that an existing collaborative filtering recommendation method is short of privacy protection in the search and modeling process of personal information, control over the degree of disclosure of the personal information by the target user is achieved, and comprehensive protection is provided for privacy data of the users.

Description

technical field [0001] The present invention relates to a collaborative filtering recommendation method based on trust-based privacy preservation in personalized search. The basic idea is: firstly, through automatic trust negotiation between the target user and the collaborative filtering recommendation system, the target user's trust in the execution behavior of the collaborative filtering recommendation system is established. Degree T cf ;Target users will be based on trust T cf To determine the degree of personal information leakage, the collaborative filtering recommendation system collects the personal information provided by the target user and clusters the users to obtain an initial set of users with similar interests to the target user; finally, the target user chooses a trust degree not lower than T cf users who are similar to yours as recommendation objects. The collaborative filtering recommendation method based on trust-based privacy preservation can make up for ...

AI Technical Summary

Problems solved by technology

[0005] The current research status shows that the target users always assume that the collaborative filtering system used is credible, but the fact is that the existing collaborative filtering recommendation system lacks the protection of personal information and privacy, and the recommendation system explicitly or not explicitly Inform users that personal information will be collected and utilized during the search process, but users cannot control what kind of information and to what extent the recommendation system collects their own information, and how to select neighbors with similar interests through modeling to recommend them product

Therefore, the user's personal privacy cannot be fully guaranteed in the process of collecting and modeling the user's personal information

Especially when the target user and the recommendation system are not in the same security domain, conventional access control methods (such as MAC, RBAC, etc.) cannot effectively control the behavior of the two

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