  Deep learning has brought great progress for the sequential recommendation
(SR) tasks. With the structure of advanced residual networks, sequential
recommender models can be stacked with many hidden layers, e.g., up to 100
layers on real-world SR datasets. Training such a deep network requires
expensive computation and longer training time, especially in situations when
there are tens of billions of user-item interactions. To deal with such a
challenge, we present StackRec, a simple but very efficient training framework
for deep SR models by layer stacking. Specifically, we first offer an important
insight that residual layers/blocks in a well-trained deep SR model have
similar distribution. Enlightened by this, we propose progressively stacking
such pre-trained residual layers/blocks so as to yield a deeper but
easier-to-train SR model. We validate the proposed StackRec by instantiating
with two state-of-the-art SR models in three practical scenarios and real-world
datasets. Extensive experiments show that StackRec achieves not only comparable
performance, but also significant acceleration in training time, compared to SR
models that are trained from scratch.
