Privacy enhanced matrix factorization for recommendation with local differential privacy

Privacy enhanced matrix factorization for recommendation with local differential privacy

Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender syst...

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書目詳細資料
Main Authors:	Shin, Hyejin, Kim, Sungwook, Shin, Junbum, Xiao, Xiaokui
其他作者:	School of Computer Science and Engineering
格式:	Article
語言:	English
出版:	2018
主題:	Matrix Factorization Local Differential Privacy
在線閱讀:	https://hdl.handle.net/10356/87023 http://hdl.handle.net/10220/45218
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!

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