Comparing recommender systems using synthetic data
- 27 September 2018
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM) in Proceedings of the 12th ACM Conference on Recommender Systems
Abstract
No abstract availableThis publication has 26 references indexed in Scilit:
- A k-anonymous approach to privacy preserving collaborative filteringJournal of Computer and System Sciences, 2015
- An effective privacy preserving algorithm for neighborhood-based collaborative filteringFuture Generation Computer Systems, 2014
- Synthetic Sequence Generator for Recommender Systems – Memory Biased Random Walk on a Sequence Multilayer NetworkLecture Notes in Computer Science, 2014
- The impact of data obfuscation on the accuracy of collaborative filteringExpert Systems with Applications, 2012
- A Privacy-Protecting Architecture for Collaborative Filtering via Forgery and Suppression of RatingsLecture Notes in Computer Science, 2012
- An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasetsComputational Statistics & Data Analysis, 2011
- Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic DataJournal of Computers, 2006
- Using Mahalanobis Distance-Based Record Linkage for Disclosure Risk AssessmentLecture Notes in Computer Science, 2006
- ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSIONInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002
- Toward a secure system engineering methodolgyPublished by Association for Computing Machinery (ACM) ,1998