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Recommendation systems have shown great potential to solve the informationexplosion problem and enhance user experience in various online applications,which recently present two emerging trends: (i) Collaboration: single-sidedmodel trained on-cloud (separate learning) to the device-cloud collaborativerecommendation (collaborative learning). (ii) Real-time Dynamic: the networkparameters are the same across all the instances (static model) to adaptivenetwork parameters generation conditioned on the real-time instances (dynamicmodel). The aforementioned two trends enable the device-cloud collaborative anddynamic recommendation, which deeply exploits the recommendation pattern amongcloud-device data and efficiently characterizes different instances withdifferent underlying distributions based on the cost of frequent device-cloudcommunication. Despite promising, we argue that most of the communications areunnecessary to request the new parameters of the recommendation system on thecloud since the on-device data distribution are not always changing. Toalleviate this issue, we designed a Intelligent DEvice-Cloud PArameter RequestModeL (IDEAL) that can be deployed on the device to calculate the requestrevenue with low resource consumption, so as to ensure the adaptivedevice-cloud communication with high revenue. We envision a new deviceintelligence learning task to implement IDEAL by detecting the dataout-of-domain. Moreover, we map the user's real-time behavior to a normaldistribution, the uncertainty is calculated by the multi-sampling outputs tomeasure the generalization ability of the device model to the current userbehavior. Our experimental study demonstrates IDEAL's effectiveness andgeneralizability on four public benchmarks, which yield a higher efficientdevice-cloud collaborative and dynamic recommendation paradigm.

IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System