A collaborative filtering recommendation method and system based on adaptive noise adding privacy protection

By using DBSCAN and box plots to filter out abnormal users, and combining adaptive noise addition technology with the k-means algorithm, the problem of uneven noise addition in collaborative filtering recommendations is solved, thereby improving recommendation accuracy and privacy protection.

CN117972225BActive Publication Date: 2026-07-24BEIJING UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
BEIJING UNIV OF TECH
Filing Date
2024-01-09
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing collaborative filtering recommendation algorithms suffer from privacy risks such as uneven noise addition leading to reduced data availability, decreased accuracy of recommendation results, and privacy leakage, making it difficult to provide accurate recommendations while protecting privacy.

Method used

The DBSCAN algorithm is used to screen abnormal users, and box plots are used to identify truly abnormal users. Noise of different magnitudes is added based on user similarity values. The k-means algorithm is used to select centroids and iteratively update them. An adaptive noise matrix is ​​constructed to cluster users and finally generate recommendation results.

Benefits of technology

It improves the accuracy of recommendation results, avoids the accumulation of noise during the iteration process, enhances user privacy protection, and reduces the risk of privacy leakage.

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Abstract

The application discloses a collaborative filtering recommendation method and system based on adaptive noise adding privacy protection, and the method comprises the following steps: suspected abnormal users are screened out by using a DBSCAN algorithm, and true abnormal users are determined by using a box chart, so that abnormal user data in a user data set is screened out; the similarity between each user is calculated, different sizes of noise are added according to the similarity value, and a user similarity noise matrix is constructed; an initial centroid is selected according to the user similarity noise matrix by using a k-means algorithm, and iterative updating is performed, so that user data clustering is realized; a recommendation list is obtained according to a neighbor set of a target user; and items are recommended to the target user according to the scores of the items in the recommendation list. Through the technical scheme of the application, the accuracy of the recommendation result is improved, the user privacy is protected, the problem that the clustering centroid deviates greatly is avoided, and the problem that noise is continuously accumulated in the iteration process to cause the final recommendation result to be inaccurate is avoided to a certain extent.
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