Abstract
As recommender systems have become increasingly popular in providing users with personalized recommendations, researchers have implemented protective measures to safeguard users’ privacy. However, the implementation of such mechanisms is extremely difficult to ensure both recommendation accuracy and privacy protection. In this paper, we propose a novel protective mechanism that addresses this challenge. Our approach introduces the concept of differential trust, which integrates matrix factorization and the combination theorem of differential privacy. We then propose the Gaussian Differential Trust Mechanism, which protects users’ historical ratings while maintaining recommendation accuracy to a certain extent. The rationality of our proposed mechanism is verified by theoretical explanation and experimental evaluation. The experiment results demonstrate that our method effectively balances the competing goals of recommendation accuracy and privacy preservation, providing a solution to the challenges faced by recommender systems.
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Notes
- 1.
From a website where we can download the data set directly. (http://trust.mindswap.org/FilmTrust).
- 2.
From an anonymized Douban dataset. Two files are included in this Douban dataset, the user-item rating file and the user social friend network file. (https://www.cse.cuhk.edu.hk/irwin.king.new/pub/data/douban).
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Acknowledgements
This work is supported in part by the National Key RD Program of China under No. 2022YFB3102100, the National Science Foundation of China under Grants U22B2027, 62172297 and 61902276, the Key Research and Development Project of Sichuan Province under Grant 2021YFSY0012, Tianjin Intelligent Manufacturing Special Fund Project under Grants 20211097, and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies under No. 2022B1212010005.
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Gong, L. et al. (2024). GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14492. Springer, Singapore. https://doi.org/10.1007/978-981-97-0811-6_5
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