  With the hardware development of mobile devices, it is possible to build the
recommendation models on the mobile side to utilize the fine-grained features
and the real-time feedbacks. Compared to the straightforward mobile-based
modeling appended to the cloud-based modeling, we propose a Slow-Fast learning
mechanism to make the Mobile-Cloud Collaborative recommendation (MC$^2$-SF)
mutual benefit. Specially, in our MC$^2$-SF, the cloud-based model and the
mobile-based model are respectively treated as the slow component and the fast
component, according to their interaction frequency in real-world scenarios.
During training and serving, they will communicate the prior/privileged
knowledge to each other to help better capture the user interests about the
candidates, resembling the role of System I and System II in the human
cognition. We conduct the extensive experiments on three benchmark datasets and
demonstrate the proposed MC$^2$-SF outperforms several state-of-the-art
methods.
