Summary of A Hybrid Meta-learning and Multi-armed Bandit Approach For Context-specific Multi-objective Recommendation Optimization, by Tiago Cunha et al.
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization
by Tiago Cunha, Andrea Marchini
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A hybrid approach called Juggler-MAB is introduced to balance multiple objectives in recommender systems for online marketplaces. The method combines meta-learning with Multi-Armed Bandits (MAB) to address limitations of existing systems. It extends the Juggler framework, which predicts optimal weights for utility and compensation adjustments using meta-learning, by incorporating a MAB component for real-time refinements. A two-stage approach is presented, where Juggler provides initial weight predictions, followed by MAB-based adjustments that adapt to changes in user behavior and market conditions. The system uses contextual features like device type and brand to make fine-grained weight adjustments based on specific segments. The approach is evaluated using a simulation framework and dataset from Expedia’s lodging booking platform, showing NDCG improvements of 2.9%, a 13.7% reduction in regret, and a 9.8% improvement in best arm selection rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Juggler-MAB is a new way to help online marketplaces recommend things that people will like. Right now, these systems have trouble balancing what different people want. Customers want good recommendations, providers want to make money, and the platform wants to keep users happy. Juggler-MAB combines two powerful tools: meta-learning (which learns how to learn) and Multi-Armed Bandits (which makes decisions based on experience). This system is better than others because it can adapt quickly to changes in what people like. |
Keywords
» Artificial intelligence » Meta learning
