Towards a new possibilistic collaborative filtering approach. - Manel Slokom; Raouia Ayachi (CSCESM, 2015)

A New Social Recommender System Based on Link Prediction Across Heterogeneous Networks. - Manel Slokom; Raouia Ayachi (KES-IDT (2), 2017)

Comparing recommender systems using synthetic data. - Manel Slokom (RecSys, 2018)

Up Close, but not too Personal: Hypotargeting for Recommender Systems. - Martha A. Larson; Manel Slokom (ImpactRS@RecSys, 2019)

Data Masking for Recommender Systems: Prediction Performance and Rating Hiding. - Manel Slokom; Martha A. Larson; Alan Hanjalic (RecSys (Late-Breaking Results), 2019)

BlurM(or)e: Revisiting Gender Obfuscation in the User-Item Matrix. - Christopher Strucks; Manel Slokom; Martha A. Larson (RMSE@RecSys, 2019)

Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding. - Manel Slokom; Martha A. Larson; Alan Hanjalic (CoRR, 2020)

SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research. - Michael D. Ekstrand; Allison Chaney; Pablo Castells; Robin Burke; David Rohde; Manel Slokom (RecSys, 2021)

Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems. - Manel Slokom; Martha A. Larson (CoRR, 2021)

Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles. - Manel Slokom; Alan Hanjalic; Martha A. Larson (Inf. Process. Manag., 2021)

When Machine Learning Models Leak: An Exploration of Synthetic Training Data. - Manel Slokom; Peter-Paul de Wolf; Martha A. Larson (PSD, 2022)

Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation. - Manel Slokom; Özlem Özgöbek; Martha A. Larson (CoRR, 2022)

Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews. - Danny Stax; Manel Slokom; Martha A. Larson (CoRR, 2022)

Machine Learning Meets Data Modification - The Potential of Pre-processing for Privacy Enchancement. - Giuseppe Garofalo; Manel Slokom; Davy Preuveneers; Wouter Joosen; Martha A. Larson (Security and Artificial Intelligence, 2022)

Exploring Privacy-Preserving Techniques on Synthetic Data as a Defense Against Model Inversion Attacks. - Manel Slokom; Peter-Paul de Wolf; Martha A. Larson (ISC, 2023)

Technological Innovation in the Media Sector: Understanding Current Practices and Unraveling Opportunities. - Simone Ooms; Pablo César; Abdallah El Ali; Davide Ceolin; Laura Hollink; Manel Slokom; Eric J. Pauwels; Valentin Robu; Han La Poutré (CHI Extended Abstracts, 2024)

A Case Study Exploring Data Synthesis Strategies on Tabular vs. Aggregated Data Sources for Official Statistics. - Mohamed Aghaddar; Liu Nuo Su; Manel Slokom; Lucas Barnhoorn; Peter-Paul de Wolf (PSD, 2024)

Relational Or Single: A Comparative Analysis of Data Synthesis Approaches for Privacy and Utility on a Use Case from Statistical Office. - Manel Slokom; Shruti Agrawal; Nynke C. Krol; Peter-Paul de Wolf (PSD, 2024)

How to Diversify any Personalized Recommender? A User-centric Pre-processing approach. - Manel Slokom; Laura Hollink (CoRR, 2024)

On the challenges of studying bias in Recommender Systems: A UserKNN case study. - Savvina Daniil; Manel Slokom; Mirjam Cuper; Cynthia C. S. Liem; Jacco van Ossenbruggen; Laura Hollink (CoRR, 2024)

Towards Purpose-aware Privacy-Preserving Techniques for Predictive Applications. - Manel Slokom (2024)

How to Diversify any Personalized Recommender? - Manel Slokom; Savvina Daniil; Laura Hollink (ECIR (4), 2025)

FedFlex: Federated Learning for Diverse Netflix Recommendations. - Sven Lankester; Manel Slokom; Gustavo de Carvalho Bertoli; Matias Vizcaino; Emmanuelle Beauxis-Aussalet; Laura Hollink (CoRR, 2025)