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Deep Neural Networks for YouTube Recommendations

Authors:

Paul Covington,

Emre SarginAuthors Info & Claims

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

Pages 191 - 198

https://doi.org/10.1145/2959100.2959190

Published: 07 September 2016 Publication History

Abstract

YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.

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Cited By

Liang SKong QLei YLi C(2025)Graphical contrastive learning for multi-interest sequential recommendationExpert Systems with Applications10.1016/j.eswa.2024.125285259(125285)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125285
Yang SZhao HLuo W(2024)The Impact of Artificial Intelligence on Intercultural CommunicationUnderstanding Multiculturalism and Interculturalism in Cross Cultures [Working Title]10.5772/intechopen.1006172Online publication date: 30-Jul-2024
https://doi.org/10.5772/intechopen.1006172
Çetin Genç Ç(2024)Trigeminal Nevralji ile İlgili Türkçe YouTube™ Videolarının Yararlılık Düzeyinin DeğerlendirilmesiADO Klinik Bilimler Dergisi10.54617/adoklinikbilimler.133381013:3(472-480)Online publication date: 24-Sep-2024
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Index Terms

Deep Neural Networks for YouTube Recommendations
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems

September 2016

490 pages

ISBN:9781450340359

DOI:10.1145/2959100

General Chairs:
Shilad Sen
Macalester College, USA
,
Werner Geyer
IBM Research Cambridge, USA
,
Program Chairs:
Jill Freyne
CSIRO, Australia
,
Pablo Castells
Universidad Autónoma de Madrid, Spain

Copyright © 2016 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 September 2016

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RecSys '16

Sponsor:

SIGCHI

RecSys '16: Tenth ACM Conference on Recommender Systems

September 15 - 19, 2016

Massachusetts, Boston, USA

Acceptance Rates

RecSys '16 Paper Acceptance Rate 29 of 159 submissions, 18%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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RecSys '24

Sponsor:
sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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Cited By

Liang SKong QLei YLi C(2025)Graphical contrastive learning for multi-interest sequential recommendationExpert Systems with Applications10.1016/j.eswa.2024.125285259(125285)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125285
Yang SZhao HLuo W(2024)The Impact of Artificial Intelligence on Intercultural CommunicationUnderstanding Multiculturalism and Interculturalism in Cross Cultures [Working Title]10.5772/intechopen.1006172Online publication date: 30-Jul-2024
https://doi.org/10.5772/intechopen.1006172
Çetin Genç Ç(2024)Trigeminal Nevralji ile İlgili Türkçe YouTube™ Videolarının Yararlılık Düzeyinin DeğerlendirilmesiADO Klinik Bilimler Dergisi10.54617/adoklinikbilimler.133381013:3(472-480)Online publication date: 24-Sep-2024
https://doi.org/10.54617/adoklinikbilimler.1333810
Prontera PPrusciano FLattarulo MUtano ESchiralli FSciorio CRomano LGrossi F(2024)Quality of bladder cancer treatment information on YouTube: May the user’s profile affect the quality of results?Archivio Italiano di Urologia e Andrologia10.4081/aiua.2024.1217996:1Online publication date: 16-Feb-2024
https://doi.org/10.4081/aiua.2024.12179
Li W(2024)Analyzing the Impact of Information Features on User Continuance Intent in Recommendation SystemsInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.35390520:1(1-36)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.4018/IJSWIS.353905
Dauphin F(2024)Normes de genre et subversions : le rôle des influenceuses dans le body positivisme et le féminisme numériqueCommunication10.4000/127l6Vol. 41/1Online publication date: 2024
https://doi.org/10.4000/127l6
Le Merrer EPenzo CTredan GVerney L(2024)Challenges in archiving the personalized webExploring the Archived Web during a Highly Transformative Age10.36253/979-12-215-0413-2.10Online publication date: 2024
https://doi.org/10.36253/979-12-215-0413-2.10
Dong JZhou YHao SFeng DZheng HXu Z(2024)Improving Graph Collaborative Filtering from the Perspective of User–Item Interaction Directly Using Contrastive LearningMathematics10.3390/math1213205712:13(2057)Online publication date: 30-Jun-2024
https://doi.org/10.3390/math12132057
Li MLian YZhu JLin JWan JSun Y(2024)A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation SystemsMathematics10.3390/math1202024712:2(247)Online publication date: 12-Jan-2024
https://doi.org/10.3390/math12020247
Huang JXie ZZhang HYang BDi CHuang R(2024)Enhancing Knowledge-Aware Recommendation with Dual-Graph Contrastive LearningInformation10.3390/info1509053415:9(534)Online publication date: 2-Sep-2024
https://doi.org/10.3390/info15090534
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