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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