research-article

CoRec: An Efficient Internet Behavior-based Recommendation Framework with Edge-cloud Collaboration on Deep Convolution Neural Networks

Authors:

Cen ChenAuthors Info & Claims

ACM Transactions on Sensor Networks, Volume 19, Issue 2

Article No.: 24, Pages 1 - 28

https://doi.org/10.1145/3526191

Published: 20 December 2022 Publication History

Abstract

Both accurate and fast mobile recommendation systems based on click behaviors analysis are crucial in e-business. Deep learning has achieved state-of-the-art accuracy and the traditional wisdom often hosts these computation-intensive models in powerful cloud centers. However, the cloud-only approaches put significant computational pressure on cloud servers and increase the latency in heavy-load scenarios. Moreover, existing work often adopts RNN structures to model behaviors that suffer from low processing speed for under-utilization of parallel devices such as GPUs. In this work, we propose an efficient internet behavior-based recommendation framework with edge-cloud collaboration on deep CNNs (CoRec) to improve both the accuracy and speed for mobile recommendation. A novel convolutional interest network (CIN) improves the accuracy by modeling the long- and short-term interests and accelerates the prediction through parallel-friendly convolutions. To further improve the serving throughput and latency, a novel device-cloud collaboration strategy reduces workloads by pre-computing and caching long-term interests in the cloud offline and real-time computation of short-term interests in devices. Extensive experiments on real-world datasets show that CoRec significantly outperforms the state-of-the-art methods in accuracy and has achieved at least an order of magnitude improvement in latency and throughput compared to cloud-only RNN-based approaches for long behaviors.

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CoRec: An Efficient Internet Behavior-based Recommendation Framework with Edge-cloud Collaboration on Deep Convolution Neural Networks

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 19, Issue 2

May 2023

599 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3575873

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 20 December 2022

Online AM: 28 July 2022

Accepted: 11 March 2022

Revised: 21 January 2022

Received: 07 August 2021

Published in TOSN Volume 19, Issue 2

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Key R&D Program of China
National Outstanding Youth Science Program of National Natural Science Foundation of China
International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China
Singapore-China NRF-NSFC Grant
Natural Science Foundation of Hunan Province
Cultivation of Shenzhen Excellent Technological and Innovative Talents
National Natural Science Foundation of China
Cultivation of Shenzhen Excellent Technological and Innovative Talents
Basic research of Shenzhen Science and technology Plan

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https://dl.acm.org/doi/10.1145/3664816
Zhang PLiu MSong XCao DGao ZNie L(2024)Universal Relocalizer for Weakly Supervised Referring Expression GroundingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365604520:7(1-23)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3656045
Hsu WLin H(2024)Context-detail-aware United Network for Single Image DerainingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363940720:5(1-18)Online publication date: 22-Jan-2024
https://dl.acm.org/doi/10.1145/3639407
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