  Deep learning has brought significant breakthroughs in sequential
recommendation (SR) for capturing dynamic user interests. A series of recent
research revealed that models with more parameters usually achieve optimal
performance for SR tasks, inevitably resulting in great challenges for
deploying them in real systems. Following the simple assumption that light
networks might already suffice for certain users, in this work, we propose
CANet, a conceptually simple yet very scalable framework for assigning adaptive
network architecture in an input-dependent manner to reduce unnecessary
computation. The core idea of CANet is to route the input user behaviors with a
light-weighted router module. Specifically, we first construct the routing
space with various submodels parameterized in terms of multiple model
dimensions such as the number of layers, hidden size and embedding size. To
avoid extra storage overhead of the routing space, we employ a weight-slicing
schema to maintain all the submodels in exactly one network. Furthermore, we
leverage several solutions to solve the discrete optimization issues caused by
the router module. Thanks to them, CANet could adaptively adjust its network
architecture for each input in an end-to-end manner, in which the user
preference can be effectively captured. To evaluate our work, we conduct
extensive experiments on benchmark datasets. Experimental results show that
CANet reduces computation by 55 ~ 65% while preserving the accuracy of the
original model. Our codes are available at
https://github.com/icantnamemyself/CANet.
