MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
This addresses the challenge of personalized recommendations for users in systems with diverse data, though it is incremental as it builds on existing meta-learning and model selection techniques.
The paper tackles the problem of heterogeneous user data in recommender systems by proposing a meta-learning framework for user-level adaptive model selection, achieving improvements in AUC and LogLoss over baselines on public and production datasets.
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.