A Privacy-Protecting Architecture for Collaborative Filtering via Forgery and Suppression of Ratings-Reference-Cited by-同舟云学术

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A Privacy-Protecting Architecture for Collaborative Filtering via Forgery and Suppression of Ratings

Published:2012 Issue: Volume: Page:42-57
ISSN:0302-9743
Container-title:Data Privacy Management and Autonomous Spontaneus Security
language:
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Author:

Parra-Arnau Javier,Rebollo-Monedero David,Forné Jordi

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-28879-1_4

Reference42 articles.

1. Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

2. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. (January 2009)

3. Amazon.com, http://www.amazon.com

4. Movielens, http://movielens.umn.edu

5. Netflix, http://www.netflix.com

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1. A Combined Approach For Collaborative Filtering Based Recommender Systems with Matrix Factorisation and Outlier Detection;Journal of Business Analytics;2021-07-03

2. Privacy Vulnerability of NeNDS Collaborative Filtering;Lecture Notes in Computer Science;2021

3. Protecting Private Attributes in App Based Mobile User Profiling;IEEE Access;2020

4. Performance Analysis of Collaborative Recommender System: A Heuristic Approach;Communications in Computer and Information Science;2019

5. Comparing recommender systems using synthetic data;Proceedings of the 12th ACM Conference on Recommender Systems;2018-09-27

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