A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization
This work addresses multi-stakeholder recommendation optimization for online marketplaces like Expedia, representing an incremental advancement over existing methods.
The paper tackles the challenge of balancing multiple objectives in recommender systems for online marketplaces by introducing Juggler-MAB, a hybrid approach combining meta-learning and Multi-Armed Bandits, which outperforms the original Juggler model with improvements such as a 2.9% increase in NDCG and a 13.7% reduction in regret.
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.