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cs.LG2026
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…
cs.IR2026
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…
cs.IR2026
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…