research-article

Deep Learning-based Short Video Recommendation and Prefetching for Mobile Commuting Users

Authors:

Jinyao YanAuthors Info & Claims

NEAT'19: Proceedings of the ACM SIGCOMM 2019 Workshop on Networking for Emerging Applications and Technologies

Pages 49 - 55

https://doi.org/10.1145/3341558.3342205

Published: 14 August 2019 Publication History

Abstract

Mobile short video application is growing rapidly and it is quickly occupying people's life. In this paper, we consider an emerging yet common scenario of short video application usage: mobile users watching short videos on their daily commuting trip on high speed public transport, where the network condition is unsatisfactory. To reduce users waiting time and improve the QoE, we propose a deep learning-based data recommendation and prefetching scheme which obtains user interests and pushes the preferred short video content to the most likely base station that users will be connected to. We use Principal Component Analysis (PCA) plus dropout to reduce the feature dimensions of Inception structure to improve the short video recommendation speed without degrading the accuracy. Through experimental evaluations, we show that the proposed scheme can effectively recommend short video and predict user trajectory, with a recall rate of 100%.

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Cited By

Wu YFu RXing TYu ZYin F(2025)A user behavior-aware multi-task learning model for enhanced short video recommendationNeurocomputing10.1016/j.neucom.2024.129076617(129076)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129076
Balakrishna CMani DBasi Reddy AChandrakanth PSelvan R(2023)Two-Stage Deep Learning - YouTube Video Recommendation Process2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS)10.1109/ICCAMS60113.2023.10525842(1-7)Online publication date: 27-Oct-2023
https://doi.org/10.1109/ICCAMS60113.2023.10525842
Ye JXiong HGuo JMeng X(2023)A group recommender system for books based on fine-grained classification of commentsThe Electronic Library10.1108/EL-11-2022-025241:2/3(326-346)Online publication date: 1-May-2023
https://doi.org/10.1108/EL-11-2022-0252
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Index Terms

Deep Learning-based Short Video Recommendation and Prefetching for Mobile Commuting Users
1. Networks
  1. Network properties
    1. Network mobility
  2. Network types
    1. Mobile networks

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cover image ACM Conferences

NEAT'19: Proceedings of the ACM SIGCOMM 2019 Workshop on Networking for Emerging Applications and Technologies

August 2019

61 pages

ISBN:9781450368766

DOI:10.1145/3341558

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

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Publication History

Published: 14 August 2019

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Research-article
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Refereed limited

Funding Sources

the Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Hubei Provincial Natural Science Foundation

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SIGCOMM '19

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SIGCOMM

SIGCOMM '19: ACM SIGCOMM 2019 Conference

August 19, 2019

Beijing, China

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NEAT'19 Paper Acceptance Rate 8 of 18 submissions, 44%;

Overall Acceptance Rate 8 of 18 submissions, 44%

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Cited By

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https://doi.org/10.1016/j.neucom.2024.129076
Balakrishna CMani DBasi Reddy AChandrakanth PSelvan R(2023)Two-Stage Deep Learning - YouTube Video Recommendation Process2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS)10.1109/ICCAMS60113.2023.10525842(1-7)Online publication date: 27-Oct-2023
https://doi.org/10.1109/ICCAMS60113.2023.10525842
Ye JXiong HGuo JMeng X(2023)A group recommender system for books based on fine-grained classification of commentsThe Electronic Library10.1108/EL-11-2022-025241:2/3(326-346)Online publication date: 1-May-2023
https://doi.org/10.1108/EL-11-2022-0252
Taleb TBenzaïd CAddad RSamdanis K(2023)AI/ML for beyond 5G systems: Concepts, technology enablers & solutionsComputer Networks10.1016/j.comnet.2023.110044237(110044)Online publication date: Dec-2023
https://doi.org/10.1016/j.comnet.2023.110044
Qian SXie YPan ZZhang YLin TMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)DAMProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3551573(7030-7034)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3551573
Edwards DRawat DSadler B(2022)Adversarial Promotion for Video based Recommender Systems2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)10.1109/CogMI56440.2022.00028(134-138)Online publication date: Dec-2022
https://doi.org/10.1109/CogMI56440.2022.00028
Zhang HBan YZhang XGuo ZXu ZMeng SLi JWang Y(2020)APL: Adaptive Preloading of Short Video with Lyapunov Optimization2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)10.1109/VCIP49819.2020.9301886(13-16)Online publication date: 1-Dec-2020
https://doi.org/10.1109/VCIP49819.2020.9301886
Huang LLu LHua W(2020)A Survey on Next-Cell Prediction in Cellular Networks: Schemes and ApplicationsIEEE Access10.1109/ACCESS.2020.30360708(201468-201485)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3036070

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