Manel Slokom

Manel Slokom is a 4th year PhD student at Delft University of Technology, The Netherlands.
Her research focuses on purpose-aware privacy-preserving data for recommender systems.
She works on generating synthetic data that protect users' information while maintaining the quality of recommendation.
At the beginning of her PhD, she worked on generating partially synthetic data and now she is exploring and testing fully synthetic data for recommender systems.
She is also interested in fairness.
She served as a student volunteer at RecSys for three years (2018, 2019 and 2020).
In 2021, she will be a student volunteer co-chair and co-organizer of the 1st Workshop on Simulation and Synthetic Data for Recommender Systems (SimuRec) part of RecSys’2021.


Publications:

Conferences and Workshops:

  • M. Slokom. Comparing recommender systems using synthetic data. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys’18). 2018 (pp. 548-552).
  • M. Slokom, M. Larson, A. Hanjalic, Data masking for recommender systems: Prediction performance and rating hiding, Late breaking results, in conjunction with the 13th ACM Conference on Recommender Systems (RecSys’19) (2019).
  • C. Strucks, M. Slokom, M. Larson, Blurm (or) e: Revisiting gender obfuscation in the user-item matrix, in: Recommendation in Multi-stakeholder Environments (RMSE), in conjunction with the 13th ACM Conference on Recommender Systems (RecSys’19), 2019.
  • M. Larson, M. Slokom. Up to Close but Not too personal. in: ImpactRS workshop in conjunction with the 13th ACM Conference on Recommender Systems (RecSys’19), 2019.
  • M. Slokom, M. Larson, & A. Hanjalic,. Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding. in the USB/INTRANET proceedings of the International Conference on Privacy in Statistical Databases. 2020.

Book chapters:

  • M. Larson, J. Choi, M. Slokom, Z. Erkin, G. Friedland, A. P. De Vries. Privacy and Audiovisual Content: Protecting Users as Big Multimedia Data Grows Bigger. In: Vrochidis, S. (ed.), Big Data Analytics for Large-Scale Multimedia Search, pp. 183-208. 2019.
  • G. Garofalo, M. Slokom, M. Larson. Machine Learning Meets Data Modification: the Potential of Pre-Processing for Privacy Enhancement. 2021 (to appear)

     
  • For future publications, please, visit Google Scholar:  link scholar.google.com/citations
  • GitHub: ManelSlokom link github.com/SlokomManel/
  • Twitter: ManelSlokom link twitter.com/ManelSlokom

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