Neighborhood recommendation method based on
information differential protection
1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China; 2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
Abstract： To reduce the leak of user privacy data in recommendation system during mining user behavior big data, the differential privacy protection was combined with the collaborative filtering recommendation algorithm to construct a differential privacy protection model. The collaborative filtering model of neighborhoods with bias term optimization was introduced. By designing average value calculation, bias term calculation, neighbor selection and similarity calculation in the calculation process of recommendation model, a neighborhood recommendation algorithm was proposed based on differential privacy protection. The algorithms of IA, BasicKNN, BiasedKNN and PPKNN were compared by experiments. The results show that the proposed differential privacy protection collaborative filtering algorithm can achieve better recommendation accuracy and ensure differential privacy protection. The proposed algorithm can obtain good recommendation effect with slight sacrificing of privacy protection effect.
马彪, 李千目. 基于信息差分保护的邻域推荐方法[J]. 江苏大学学报（自然科学版）, 2019, 40(4): 439-445.
MA Biao, LI Qian-Mu. Neighborhood recommendation method based on
information differential protection[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2019, 40(4): 439-445.
2019, Vol. 40 
