MC$^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation
This work addresses recommendation systems for mobile users by improving accuracy through collaboration, but it appears incremental as it builds on existing mobile-cloud approaches.
The paper tackles the problem of mobile-cloud collaborative recommendation by proposing a slow-fast learning mechanism, where cloud and mobile models communicate to capture user interests, and demonstrates that it outperforms state-of-the-art methods on three benchmark datasets.
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.