Comparing recommender systems using synthetic data

Manel Slokom*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)


In this work, we propose SynRec, a data protection framework that uses data synthesis. The goal is to protect sensitive information in the user-item matrix by replacing the original values with synthetic values or, alternatively, completely synthesizing new users. The synthetic data must fulfill two requirements. First, it must no longer be possible to derive certain sensitive information from the data, and, second, it must remain possible to use the synthetic data for comparing recommender systems. SynRec is a step towards making it possible for companies to release recommender system data to the research community for the development of new algorithms, for example, in the context of recommender system challenges. We report the results of preliminary experiments, which provide a proof-of-concept, and also describe the future research directions, i.e., the challenges that must be addressed in order to make the framework useful in practice.

Original languageEnglish
Title of host publicationRecSys '18
Subtitle of host publicationProceedings of the 12th ACM Conference on Recommender Systems
Place of PublicationNew York, NY
PublisherAssociation for Computer Machinery
Number of pages5
ISBN (Print)978-1-4503-5901-6
Publication statusPublished - 2018
Event12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada
Duration: 2 Oct 20187 Oct 2018


Conference12th ACM Conference on Recommender Systems, RecSys 2018


  • Disclosure control
  • Preference hiding
  • Recommendation
  • Synthetic data


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