  In collaborative filtering (CF) algorithms, the optimal models are usually
learned by globally minimizing the empirical risks averaged over all the
observed data. However, the global models are often obtained via a performance
tradeoff among users/items, i.e., not all users/items are perfectly fitted by
the global models due to the hard non-convex optimization problems in CF
algorithms. Ensemble learning can address this issue by learning multiple
diverse models but usually suffer from efficiency issue on large datasets or
complex algorithms. In this paper, we keep the intermediate models obtained
during global model learning as the snapshot models, and then adaptively
combine the snapshot models for individual user-item pairs using a memory
network-based method. Empirical studies on three real-world datasets show that
the proposed method can extensively and significantly improve the accuracy (up
to 15.9% relatively) when applied to a variety of existing collaborative
filtering methods.
