How to Diversify any Personalized Recommender?
Proceedings of the Advances in Information Retrieval, 2025
Towards Purpose-aware Privacy-Preserving Techniques for Predictive Applications.
PhD thesis, 2024
On the challenges of studying bias in Recommender Systems: A UserKNN case study.
CoRR, 2024
How to Diversify any Personalized Recommender? A User-centric Pre-processing approach.
CoRR, 2024
Relational Or Single: A Comparative Analysis of Data Synthesis Approaches for Privacy and Utility on a Use Case from Statistical Office.
Proceedings of the Privacy in Statistical Databases - International Conference, 2024
A Case Study Exploring Data Synthesis Strategies on Tabular vs. Aggregated Data Sources for Official Statistics.
Proceedings of the Privacy in Statistical Databases - International Conference, 2024
Technological Innovation in the Media Sector: Understanding Current Practices and Unraveling Opportunities.
Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024
Exploring Privacy-Preserving Techniques on Synthetic Data as a Defense Against Model Inversion Attacks.
Proceedings of the Information Security - 26th International Conference, 2023
Machine Learning Meets Data Modification - The Potential of Pre-processing for Privacy Enchancement.
Security and Artificial Intelligence, 2022
Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews.
CoRR, 2022
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation.
CoRR, 2022
When Machine Learning Models Leak: An Exploration of Synthetic Training Data.
Proceedings of the Privacy in Statistical Databases - International Conference, 2022
Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles.
Inf. Process. Manag., 2021
Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems.
CoRR, 2021
SimuRec: Workshop on Synthetic Data and Simulation Methods for Recommender Systems Research.
Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021
Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding.
CoRR, 2020
BlurM(or)e: Revisiting Gender Obfuscation in the User-Item Matrix.
Proceedings of the Workshop on Recommendation in Multi-stakeholder Environments co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019), 2019
Data Masking for Recommender Systems: Prediction Performance and Rating Hiding.
Proceedings of ACM RecSys 2019 Late-Breaking Results co-located with the 13th ACM Conference on Recommender Systems, 2019
Up Close, but not too Personal: Hypotargeting for Recommender Systems.
Proceedings of the 1st Workshop on the Impact of Recommender Systems co-located with 13th ACM Conference on Recommender Systems, 2019
Comparing recommender systems using synthetic data.
Proceedings of the 12th ACM Conference on Recommender Systems, 2018
A New Social Recommender System Based on Link Prediction Across Heterogeneous Networks.
Proceedings of the Intelligent Decision Technologies 2017 - Proceedings of the 9th KES International Conference on Intelligent Decision Technologies (KES-IDT 2017), 2017
Towards a new possibilistic collaborative filtering approach.
Proceedings of the Second International Conference on Computer Science, 2015