  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.
