Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media

The paper introduces Contextual Scalarisation Thompson Sampler (CSTS), a multi‑objective contextual bandit algorithm that learns to weight competing objectives based on observed context. It addresses the need for adaptable decision‑making in public media, where goals such as audience reach, cultural values, and operational constraints must be balanced. Experiments on Radio Télévision Suisse data demonstrate that CSTS improves contextual relevance and aligns more closely with expert curation than fixed‑weight or standard bandit methods.

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• reinforcement-learning
• safety