根据上下文动态调整目标权重，让公共媒体推荐更智能。
Contextual Scalarisation Thompson Sampling for multi-objective decisions in public media
- 基于上下文自动学习各目标权重，适应不同场景
- 在瑞士广播公司数据上提升内容相关性与专家偏好匹配度
- 适合需多目标平衡的公共媒体推荐系统
推荐系统常需在多个竞争目标间权衡，如公共媒体的内容传播范围、文化价值、公共服务使命及运营约束。现有方法依赖固定目标组合或帕累托优化，无法适应情境变化。本文提出上下文加权汤普森采样（CSTS），一种多目标上下文强化学习方法，可依据当前上下文动态调整目标权重。我们在瑞士国家广播公司（Radio Télévision Suisse）的真实节目数据上评估该方法，结果显示其在上下文相关性和与专家编辑实践的契合度上均优于固定权重法和标准上下文强化学习方法。
原文摘要 · Abstract (English)
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 contextual relevance and better alignment with expert curation practices compared to fixed weight and standard contextual bandit approaches.
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