Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media
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 Télévision Suisse, the Swiss national broadcaster, showing improved cont...
| Authors | Théo Maëtz, Luc Guillet, Andrea Cavallaro |
|---|---|
| Primary category | cs.IR |
| Published | 29 May 2026 |
| Last updated at source | 13 September 2026 |
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