research-article

Beyond the Content: Considering the Network for Online Video Recommendation

Authors:

Guo ChenAuthors Info & Claims

APNet '23: Proceedings of the 7th Asia-Pacific Workshop on Networking

Pages 150 - 156

https://doi.org/10.1145/3600061.3600075

Published: 05 September 2023 Publication History

Abstract

Online recommendation systems play critical roles in enhancing user experience by helping them find the most interesting videos from a vast amount of content. However, the existing recommendation modules and video transmission modules in the industry often operate independently, resulting in the recommendation model providing some videos that cannot be transmitted within the specified deadlines successfully. This can lead to an inferior watching experience for users and resource waste for video providers. To address this, we propose a novel framework called NetRec, which for the first time optimizes the recommendation quality by jointly considering the network transmission. We accomplish this by re-ranking the top-N videos obtained from the recommendation system and selecting the top-M (M is approximately half of N) videos that provide the maximum overall revenue, e.g., video playing time while considering the network status. The entire system comprises network measurement, video quality estimation, and multi-objective optimization modules. Real-world Internet results show that our framework can increase users’ video playing time by 20% to 160%. Furthermore, we provide several promising directions for further improving the video recommendation quality under our NetRec framework, which jointly considers the network for the recommendation.

References

[1]

Zahaib Akhtar, Yun Seong Nam, Ramesh Govindan, Sanjay G. Rao, Jessica Chen, Ethan Katz-Bassett, Bruno Ribeiro, Jibin Zhan, and Hui Zhang. 2018. Oboe: auto-tuning video ABR algorithms to network conditions. Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (2018).

Digital Library

[2]

China Internet Network Information Center. 2022. The 50th Statistical Report on China’s Internet Development. (2022). https://www.cnnic.net.cn/NMediaFile/2022/0926/MAIN1664183425619U2MS433V3V.pdf, Last accessed on 2023-03-08.

[3]

Sihao Ding, Peng Wu, Fuli Feng, Yitong Wang, Xiangnan He, Yong Liao, and Yongdong Zhang. 2022. Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD ’22). Association for Computing Machinery, New York, NY, USA, 305–315. https://doi.org/10.1145/3534678.3539240

Digital Library

[4]

Tongtong Feng, Haifeng Sun, Q. Qi, Jingyu Wang, and Jianxin Liao. 2020. Vabis: Video Adaptation Bitrate System for Time-Critical Live Streaming. IEEE Transactions on Multimedia 22 (2020), 2963–2976.

[5]

Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, and Tat-Seng Chua. 2022. KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management(CIKM ’22). 11. https://doi.org/10.1145/3511808.3557220

Digital Library

[6]

Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang, and Kun Gai. 2022. Real-Time Short Video Recommendation on Mobile Devices. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management(CIKM ’22). Association for Computing Machinery, New York, NY, USA, 3103–3112. https://doi.org/10.1145/3511808.3557065

Digital Library

[7]

Wei Guo, Can Zhang, Zhicheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, and Rui Zhang. 2022. MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). 727–740. https://doi.org/10.1109/ICDE53745.2022.00059

[8]

Kerim Hodzic, Mirsad Cosovic, Sasa Mrdovic, Jason J. Quinlan, and Darijo Raca. 2022. Realistic Video Sequences for Subjective QoE Analysis. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3524273.3532894

Digital Library

[9]

Jinsung Lee, Sungyong Lee, Jongyun Lee, Sandesh Dhawaskar Sathyanarayana, Hyoyoung Lim, Jihoon Lee, Xiaoqing Zhu, Sangeeta Ramakrishnan, Dirk Grunwald, Kyunghan Lee, and Sangtae Ha. 2020. PERCEIVE: Deep Learning-Based Cellular Uplink Prediction Using Real-Time Scheduling Patterns. In Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services(MobiSys ’20). Association for Computing Machinery, New York, NY, USA, 377–390. https://doi.org/10.1145/3386901.3388911

Digital Library

[10]

Fuyuan Lyu, Xing Tang, Huifeng Guo, Ruiming Tang, Xiuqiang He, Rui Zhang, and Xue Liu. 2022. Memorize, Factorize, or be Naive: Learning Optimal Feature Interaction Methods for CTR Prediction. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). 1450–1462. https://doi.org/10.1109/ICDE53745.2022.00113

[11]

Xuan Rao, Lisi Chen, Yong Liu, Shuo Shang, Bin Yao, and Peng Han. 2022. Graph-Flashback Network for Next Location Recommendation(KDD ’22). Association for Computing Machinery, New York, NY, USA, 1463–1471. https://doi.org/10.1145/3534678.3539383

Digital Library

[12]

Sujay S Tadahal, Sushma V Gummadi, Kartik Prajapat, S. M. Meena, Uday Kulkarni, Sunil V. Gurlahosur, and Shashidhara B. Vyakaranal. 2021. A Survey On Adaptive Bitrate Algorithms and Their Improvisations. 2021 International Conference on Intelligent Technologies (CONIT) (2021), 1–7.

[13]

Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, and Shaoping Ma. 2022. Towards Representation Alignment and Uniformity in Collaborative Filtering. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD ’22). Association for Computing Machinery, New York, NY, USA, 1816–1825. https://doi.org/10.1145/3534678.3539253

Digital Library

[14]

Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip S. Yu, and Peng Cui. 2022. Invariant Preference Learning for General Debiasing in Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD ’22). Association for Computing Machinery, New York, NY, USA, 1969–1978. https://doi.org/10.1145/3534678.3539439

Digital Library

[15]

Wikipedia. 2023. Knapsack problem — Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Knapsack%20problem&oldid=1145132664. (2023). [Online; accessed 18-March-2023].

[16]

Wikipedia. 2023. Marginal utility — Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Marginal%20utility&oldid=1135735461. (2023). [Online; accessed 18-March-2023].

[17]

Xinlei Yang, Hao Lin, Zhenhua Li, Feng Qian, Xingyao Li, Zhiming He, Xudong Wu, Xianlong Wang, Yunhao Liu, Zhi Liao, Daqiang Hu, and Tianyin Xu. 2022. Mobile Access Bandwidth in Practice: Measurement, Analysis, and Implications. In Proceedings of the ACM SIGCOMM 2022 Conference(SIGCOMM ’22). Association for Computing Machinery, New York, NY, USA, 114–128. https://doi.org/10.1145/3544216.3544237

Digital Library

[18]

Xinlei Yang, Xianlong Wang, Zhenhua Li, Yunhao Liu, Feng Qian, Liangyi Gong, Rui Miao, and Tianyin Xu. 2021. Fast and Light Bandwidth Testing for Internet Users. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). USENIX Association, 1011–1026. https://www.usenix.org/conference/nsdi21/presentation/yang-xinlei

[19]

Jiangchao Yao, Feng Wang, Xichen Ding, Shaohu Chen, Bo Han, Jingren Zhou, and Hongxia Yang. 2022. Device-Cloud Collaborative Recommendation via Meta Controller(KDD ’22). Association for Computing Machinery, New York, NY, USA, 4353–4362. https://doi.org/10.1145/3534678.3539181

Digital Library

[20]

Ruohan Zhan, Changhua Pei, Qiang Su, Jianfeng Wen, Xueliang Wang, Guanyu Mu, Dong Zheng, Peng Jiang, and Kun Gai. 2022. Deconfounding Duration Bias in Watch-Time Prediction for Video Recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD ’22). Association for Computing Machinery, New York, NY, USA, 4472–4481. https://doi.org/10.1145/3534678.3539092

Digital Library

Cited By

Yang YQi ZWu HSong QZhang TLi HTu YZhan KWang BSerra ESpezzano F(2024)Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking FrameworkProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680076(5031-5037)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680076

Index Terms

Beyond the Content: Considering the Network for Online Video Recommendation
1. Networks
  1. Network performance evaluation
    1. Network measurement
  2. Network protocols
    1. Application layer protocols

Recommendations

Content-Independent Task-Focused Recommendation

Recommender systems, also known as collaborative filtering systems, predict a user's current interest in unseen items based on ratings or recommendations from other people. Users are typically asked to rate items (such as movies) that they have already ...
Content-based recommendation systems
The adaptive web

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of ...
Jointly modeling content, social network and ratings for explainable and cold-start recommendation

Model-based approach to collaborative filtering (CF), such as latent factor models, has improved both accuracy and efficiency of predictions on large and sparse dataset. However, most existing methods still face two major problems: (1) the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

APNet '23: Proceedings of the 7th Asia-Pacific Workshop on Networking

June 2023

229 pages

ISBN:9798400707827

DOI:10.1145/3600061

Copyright © 2023 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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

APNET 2023

APNET 2023: 7th Asia-Pacific Workshop on Networking

June 29 - 30, 2023

Hong Kong, China

Acceptance Rates

Overall Acceptance Rate 50 of 118 submissions, 42%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
47
Total Downloads

Downloads (Last 12 months)39
Downloads (Last 6 weeks)1

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yang YQi ZWu HSong QZhang TLi HTu YZhan KWang BSerra ESpezzano F(2024)Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking FrameworkProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680076(5031-5037)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680076

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents