用元学习与强化学习结合，动态优化多方利益推荐结果。
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization
- 先用元学习预测权重，再用多臂老虎机实时调整，适应不同场景。
- 在Expedia数据上提升NDCG 2.9%，减少13.7%损失，更好选最优策略。
- 适合需要平衡用户、商家和平台利益的推荐系统研发者。
在线市场中的推荐系统面临平衡多方目标的挑战，需兼顾用户、商家及平台利益。本文提出Juggler-MAB，一种融合元学习与多臂老虎机（MAB）的混合方法，以克服现有系统的局限性。该方法在原有Juggler框架基础上，利用元学习预测效用与补偿调整的最优权重，并引入MAB组件实现对用户行为与市场变化的实时上下文敏感优化。通过两阶段流程：首先由Juggler提供初始权重预测，随后基于设备类型、品牌等上下文特征进行细粒度调整。我们使用来自Expedia住宿预订平台的60万次搜索数据构建仿真评估框架。结果表明，Juggler-MAB在所有指标上均优于原版Juggler模型，其中NDCG提升2.9%，后悔值降低13.7%，最佳动作选择率提高9.8%。
原文摘要 · Abstract (English)
Recommender systems in online marketplaces face the challenge of balancing multiple objectives to satisfy various stakeholders, including customers, providers, and the platform itself. This paper introduces Juggler-MAB, a hybrid approach that combines meta-learning with Multi-Armed Bandits (MAB) to address the limitations of existing multi-stakeholder recommendation systems. Our method extends the Juggler framework, which uses meta-learning to predict optimal weights for utility and compensation adjustments, by incorporating a MAB component for real-time, context-specific refinements. We present a two-stage approach where Juggler provides initial weight predictions, followed by MAB-based adjustments that adapt to rapid changes in user behavior and market conditions. Our system leverages contextual features such as device type and brand to make fine-grained weight adjustments based on specific segments. To evaluate our approach, we developed a simulation framework using a dataset of 0.6 million searches from Expedia's lodging booking platform. Results show that Juggler-MAB outperforms the original Juggler model across all metrics, with NDCG improvements of 2.9%, a 13.7% reduction in regret, and a 9.8% improvement in best arm selection rate.
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