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

תקציר מקורי באנגליתarXiv:2605.31291v2 Announce Type: replace-cross Abstract: Recommender systems may operate under multiple, competing objectives. For example, audience reach, cultural values, public service mandate, and operational constraints must be balanced in editorial decisions of public service media. Existing approaches relying on fixed combinations of objectives or Pareto-based optimisation do not adapt to changing priorities across situations. In this paper, we propose Contextual Scalarisation Thompson Sampler (CSTS), a multi-objective contextual bandit method that learns to weight objectives as a function of the observed context. We evaluate CSTS on real programming data from Radio T\'el\'evision Suisse, the Swiss national broadcaster, showing improved contextual relevance and better alignment wit