  Sequential recommender systems (SRS) have become the key technology in
capturing user's dynamic interests and generating high-quality recommendations.
Current state-of-the-art sequential recommender models are typically based on a
sandwich-structured deep neural network, where one or more middle (hidden)
layers are placed between the input embedding layer and output softmax layer.
In general, these models require a large number of parameters (such as using a
large embedding dimension or a deep network architecture) to obtain their
optimal performance. Despite the effectiveness, at some point, further
increasing model size may be harder for model deployment in resource-constraint
devices, resulting in longer responding time and larger memory footprint. To
resolve the issues, we propose a compressed sequential recommendation
framework, termed as CpRec, where two generic model shrinking techniques are
employed. Specifically, we first propose a block-wise adaptive decomposition to
approximate the input and softmax matrices by exploiting the fact that items in
SRS obey a long-tailed distribution. To reduce the parameters of the middle
layers, we introduce three layer-wise parameter sharing schemes. We instantiate
CpRec using deep convolutional neural network with dilated kernels given
consideration to both recommendation accuracy and efficiency. By the extensive
ablation studies, we demonstrate that the proposed CpRec can achieve up to
4$\sim$8 times compression rates in real-world SRS datasets. Meanwhile, CpRec
is faster during training\inference, and in most cases outperforms its
uncompressed counterpart.
