7 citations · 7 across the 11 of their papers we have counts for
11 papers
LLM-as-a-Discriminator: When Synthetic Tables Still Look Real
Manel Slokom, Malek Slokom, Thierno Kante
Privacy and data sharing are often in tension. Many organizations use synthetic data to reduce privacy risk and still share useful data. For tabular data, auditing privacy remains…
Beyond Centralization: User-Controlled Federated Recommendations in Practice
Manel Slokom, Alejandro Bellogin
Recommendation systems typically require centralized user data, limiting user control and raising privacy concerns. Federated learning offers an alternative by keeping data on-devi…
LLMDiRec: LLM-Enhanced Intent Diffusion for Sequential Recommendation
Bo-Chian Chen, Manel Slokom
Existing sequential recommendation models, even advanced diffusion-based approaches, often struggle to capture the rich semantic intent underlying user behavior, especially for new…
FedFlex: Federated Learning for Diverse Netflix Recommendations
Sven Lankester, Gustavo de Carvalho Bertoli, Matias Vizcaino +2
The drive for personalization in recommender systems creates a tension between user privacy and the risk of "filter bubbles". Although federated learning offers a promising paradig…
On the challenges of studying bias in Recommender Systems: A UserKNN case study
Savvina Daniil, Manel Slokom, Mirjam Cuper +3
Statements on the propagation of bias by recommender systems are often hard to verify or falsify. Research on bias tends to draw from a small pool of publicly available datasets an…
How to Diversify any Personalized Recommender?
Manel Slokom, Savvina Danil, Laura Hollink
In this paper, we introduce a novel approach to improve the diversity of Top-N recommendations while maintaining accuracy. Our approach employs a user-centric pre-processing strate…
When Machine Learning Models Leak: An Exploration of Synthetic Training Data
Manel Slokom, Peter-Paul de Wolf, Martha Larson
We investigate an attack on a machine learning model that predicts whether a person or household will relocate in the next two years, i.e., a propensity-to-move classifier. The att…
Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews
Danny Stax, Manel Slokom, Martha Larson
Recently, researchers have turned their attention to recommender systems that use only minimal necessary data. This trend is informed by the idea that recommender systems should us…
Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation
Manel Slokom, Özlem Özgöbek, Martha Larson
This paper studies user attributes in light of current concerns in the recommender system community: diversity, coverage, calibration, and data minimization. In experiments with a…
Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems
Manel Slokom, Martha Larson
We present a case that the newly emerging field of synthetic data in the area of recommender systems should prioritize `doing data right'. We consider this catchphrase to have two…
Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
Manel Slokom, Martha Larson, Alan Hanjalic
This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems. Specifically, we use a synthetic data generation…