  Recommender Systems (RSs) in real-world applications often deal with billions
of user interactions daily. To capture the most recent trends effectively, it
is common to update the model incrementally using only the newly arrived data.
However, this may impede the model's ability to retain long-term information
due to the potential overfitting and forgetting issues. To address this
problem, we propose a novel Adaptive Sequential Model Generation (ASMG)
framework, which generates a better serving model from a sequence of historical
models via a meta generator. For the design of the meta generator, we propose
to employ Gated Recurrent Units (GRUs) to leverage its ability to capture the
long-term dependencies. We further introduce some novel strategies to apply
together with the GRU meta generator, which not only improve its computational
efficiency but also enable more accurate sequential modeling. By instantiating
the model-agnostic framework on a general deep learning-based RS model, we
demonstrate that our method achieves state-of-the-art performance on three
public datasets and one industrial dataset.
