# A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization

arXiv: https://arxiv.org/abs/2409.08752 · HTML: https://mlsanity.com/paper/2409.08752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders](https://mlsanity.com/paper/1708.00651/) (2017)
- [CF4CF: Recommending Collaborative Filtering algorithms using Collaborative Filtering](https://mlsanity.com/paper/1803.02250/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Production-Ready RL Framework for Personalized Utility Tuning with Pareto Sweeping in Pinterest Recommender Systems](https://mlsanity.com/paper/2605.16344/) (2026)

## Abstract (arXiv)

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.