Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores

arXiv:2609.29453 · cs.LG, cs.IR · Submitted 2026-09-24 · Read on arXiv

cs.LG, cs.IR

Submitted: 2026-09-24

Updated: 2026-09-24

Comments: 20 pages including supplementary appendix. Accepted at ACM RecSys 2026

DOI: 10.1145/3773078.3831836

Code: https://github.com/mlgorithm/decoupled-slate-recommendation-recsys-2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 26× in this paper · explained in 2 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Regret — 7× in this paper · explained in 3 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Differentially Private — 5× in this paper · explained in 3 episodes
• Differential Privacy — 4× in this paper · explained in 4 episodes
• Admissibility — 3× in this paper · explained in 2 episodes
• Decoupled Learning — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Conference on Knowledge Discovery and Data Mining — 2× in this paper
• Contextual Bandits — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

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