  Deep learning has brought great progress for the sequential recommendation
(SR) tasks. With advanced network architectures, sequential recommender models
can be stacked with many hidden layers, e.g., up to 100 layers on real-world
recommendation datasets. Training such a deep network is difficult because it
can be computationally very expensive and takes much longer time, especially in
situations where there are tens of billions of user-item interactions. To deal
with such a challenge, we present StackRec, a simple, yet very effective and
efficient training framework for deep SR models by iterative layer stacking.
Specifically, we first offer an important insight that hidden layers/blocks in
a well-trained deep SR model have very similar distributions. Enlightened by
this, we propose the stacking operation on the pre-trained layers/blocks to
transfer knowledge from a shallower model to a deep model, then we perform
iterative stacking so as to yield a much deeper but easier-to-train SR model.
We validate the performance of StackRec by instantiating it with four
state-of-the-art SR models in three practical scenarios with real-world
datasets. Extensive experiments show that StackRec achieves not only comparable
performance, but also substantial acceleration in training time, compared to SR
models that are trained from scratch. Codes are available at
https://github.com/wangjiachun0426/StackRec.
