Data science challenges allow companies, and other data holders, to collaborate with the wider research community. In the area of recommender systems, the potential of such challenges to move forward the state of the art is limited due to concerns about releasing user interaction data. This paper investigates the potential of privacy-preserving data publishing for supporting recommender system challenges. We propose a data masking algorithm, Shuffle-NNN, with two steps: Neighborhood selection and value swapping. Neighborhood selection preserves valuable item similarity information. The data shuffling technique hides (i.e., changes) ratings of users for individual items. Our experimental results demonstrate that the relative performance of algorithms, which is the key property that a data science challenge must measure, is comparable between the original data and the data masked with Shuffle-NNN.

Original languageEnglish
Pages (from-to)21-25
Number of pages5
JournalCEUR Workshop Proceedings
Volume2431
Publication statusPublished - 2019
Event2019 ACM Conference on Recommender Systems Late-breaking Results, ACM RecSys LBR 2019 - Copenhagen, Denmark
Duration: 16 Sep 201920 Sep 2019

    Research areas

  • Data masking, Privacy-preserving data publishing, Recommender systems

ID: 66776818