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

• URL: http://arxiv.org/abs/2302.07335v1
• Date: Tue, 14 Feb 2023 20:44:12 GMT
• Title: IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System
• Authors: Zheqi Lv, Zhengyu Chen, Shengyu Zhang, Kun Kuang, Wenqiao Zhang, Mengze Li, Beng Chin Ooi, Fei Wu
• Abstract summary: Two trends enable the device-cloud collaborative and dynamic recommendation. We design a new device intelligence task to implement I by detecting the data out-of-domain. Our study demonstrates Is effectiveness and generalizability on four public benchmarks.
• Score: 48.04687384069841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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