Recommendation systems have shown great potential to solve the information
explosion problem and enhance user experience in various online applications,
which recently present two emerging trends: (i) Collaboration: single-sided
model trained on-cloud (separate learning) to the device-cloud collaborative
recommendation (collaborative learning). (ii) Real-time Dynamic: the network
parameters are the same across all the instances (static model) to adaptive
network parameters generation conditioned on the real-time instances (dynamic
model). The aforementioned two trends enable the device-cloud collaborative and
dynamic recommendation, which deeply exploits the recommendation pattern among
cloud-device data and efficiently characterizes different instances with
different underlying distributions based on the cost of frequent device-cloud
communication. Despite promising, we argue that most of the communications are
unnecessary to request the new parameters of the recommendation system on the
cloud since the on-device data distribution are not always changing. To
alleviate this issue, we designed a Intelligent DEvice-Cloud PArameter Request
ModeL (IDEAL) that can be deployed on the device to calculate the request
revenue with low resource consumption, so as to ensure the adaptive
device-cloud communication with high revenue. We envision a new device
intelligence learning task to implement IDEAL by detecting the data
out-of-domain. Moreover, we map the user's real-time behavior to a normal
distribution, the uncertainty is calculated by the multi-sampling outputs to
measure the generalization ability of the device model to the current user
behavior. Our experimental study demonstrates IDEAL's effectiveness and
generalizability on four public benchmarks, which yield a higher efficient
device-cloud collaborative and dynamic recommendation paradigm.

提出了一种名为 IDEAL 的智能设备 - 云参数请求模型，其可以检测数据领域外的请求收入来保证高收益的自适应设备 - 云通信，利用协作学习和动态模型来实现高效的设备 - 云协作和动态推荐。实验结果表明 IDEAL 在四个公共基准测试中具有有效性和一般性。