Improved LSH for privacy-aware and robust recommender system with sparse data in edge environment

EURASIP Journal on Wireless Communications and Networking

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[1] 0000 0001 0227 8151, grid.412638.a, School of Information Science and Engineering, Qufu Normal University, Rizhao, China;0000 0001 0227 8151, grid.412638.a, Student Affairs Office, Qufu Normal University, Rizhao, China;0000 0001 2254 3960, grid.453697.a, School of Software, University of Science and Technology Liaoning, Anshan, China;

关键词: Recommender system;    Privacy;    Robustness;    LSH;    Sparse data;    Edge;   

DOI  :  10.1186/s13638-019-1478-1

来源: publisher

【 摘 要 】

Recommender systems have become a popular and effective way to quickly discover new service items that are probably preferred by prospective users. Through analyzing the historical service usage data produced in the past, a recommender system can infer the potential user preferences and make accurate recommendations accordingly. However, in the edge environment, the service usage data stored in each edge server are often very sparse, which may result in expected cold-start problems. Besides, in the edge environment, the data required to make an optimal service recommendation decision are often stored in different edge clients or servers, which require additional privacy-preservation strategies to secure the sensitive data involved. Considering the above two drawbacks, traditional locality-sensitive hashing (LSH) is improved to be multi-probing LSH and then introduced to aid the recommendation process so as to guarantee the security and robustness of recommender systems. Experiments conducted on well-known dataset prove the effectiveness and efficiency of the work.

【 授权许可】

CC BY