Abstract
Collaborative filtering algorithm is one of the most popular recommendation algorithms. There is, however, the risk of privacy leakage when making effective recommendation. Differential privacy is a relatively new privacy protection mechanism in the field, and has been used in recommendation systems. To this end, the existing research still has some disadvantages. Particularly, they do not have satisfactory performance and have difficulty in solving the cold start scenarios. In this paper, we propose an improved differential privacy enabled collaborative filtering algorithm incorporating user information. The algorithm improves similarity calculation, and solves the user cold start problem by making effective use of user information (related attributes). Experiments show that with the same privacy guarantee, the proposed algorithm improves the performance of the recommendation system and indeed solves the problem of cold start to some good extent.
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Acknowledgement
The research was supported in part by NSF China (61872142; 61772200; 61702334; 61572318), NSF Shanghai (17ZR140690017ZR1429700), ECUST Research Fund for Education (ZH1726108), Shanghai Pu Jiang (17PJ1401900), Special Funds for Information Developing by SEIC (201602008). We thank Tao Huang for checking the paper.
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Ren, J., Xu, X., Yu, H. (2019). Improved Collaborative Filtering Algorithm Incorporating User Information and Using Differential Privacy. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_36
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DOI: https://doi.org/10.1007/978-981-15-1377-0_36
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