# MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection

arXiv: https://arxiv.org/abs/2001.10378 · HTML: https://mlsanity.com/paper/2001.10378/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 13 of 13)

- [Cross-Market Product Recommendation](https://mlsanity.com/paper/2109.05929/) (2021)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation](https://mlsanity.com/paper/2202.03851/) (2022)
- [Contextualizing Meta-Learning via Learning to Decompose](https://mlsanity.com/paper/2106.08112/) (2021)
- [Contextual Active Model Selection](https://mlsanity.com/paper/2207.06030/) (2022)
- [LiMAML: Personalization of Deep Recommender Models via Meta Learning](https://mlsanity.com/paper/2403.00803/) (2024)
- [Click-Through Rate Prediction Using Graph Neural Networks and Online Learning](https://mlsanity.com/paper/2105.03811/) (2021)
- [Learning to Learn a Cold-start Sequential Recommender](https://mlsanity.com/paper/2110.09083/) (2021)
- [Leaping Through Time with Gradient-based Adaptation for Recommendation](https://mlsanity.com/paper/2112.05914/) (2021)
- [Meta-Shop: Improving Item Advertisement For Small Businesses](https://mlsanity.com/paper/2212.01414/) (2022)
- [Towards a Theoretical Understanding of Two Tower Recommendation Models](https://mlsanity.com/paper/2403.00802/) (2024)
- [Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2406.00249/) (2024)
- [RecBundle: A Next-Generation Geometric Paradigm for Explainable Recommender Systems](https://mlsanity.com/paper/2603.16088/) (2026)

## Abstract (arXiv)

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizing the quality of recommendation for each user. We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. In this framework, a collection of recommenders is trained with data from all users, on top of which a model selector is trained via meta-learning to select the best single model for each user with the user-specific historical data. We conduct extensive experiments on two public datasets and a real-world production dataset, demonstrating that our proposed framework achieves improvements over single model baselines and sample-level model selector in terms of AUC and LogLoss. In particular, the improvements may lead to huge profit gain when deployed in online recommender systems.