We discuss the video recommendation system in use at YouTube, the world's most popular online video community. The system recommends personalized sets of videos to users based on their activity on the site. We discuss some of the unique challenges that the system faces and how we address them. In addition, we provide details on the experimentation and evaluation framework used to test and tune new algorithms. We also present some of the findings from these experiments.

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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
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The YouTube video recommendation system
1. Information systems

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Published In

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

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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Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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

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https://doi.org/10.4081/aiua.2024.12179
Cheng SHuang WYu ZZheng J(2024)MSD: Multi-Order Semantic Denoising Model for Session-Based RecommendationsElectronics10.3390/electronics1316311813:16(3118)Online publication date: 7-Aug-2024
https://doi.org/10.3390/electronics13163118
Babbar GAnand AAggrawal N(2024)Modelling & Analyzing View Growth Pattern of YouTube Videos inculcating the impact of Subscribers, Word of Mouth and Recommendation SystemsInternational Journal of Mathematical, Engineering and Management Sciences10.33889/IJMEMS.2024.9.3.0239:3(435-450)Online publication date: 1-Jun-2024
https://doi.org/10.33889/IJMEMS.2024.9.3.023
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Rendle S(2024)Efficient Optimization of Sparse User Encoder RecommendersACM Transactions on Recommender Systems10.1145/36511702:3(1-31)Online publication date: 6-Mar-2024
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Kim ECha JJeong DPark E(2024)Artwork Recommendations based on User Preferences: Integrating Clustering Analysis with Visual FeaturesJournal on Computing and Cultural Heritage 10.1145/364990117:3(1-10)Online publication date: 15-May-2024
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Zhao HCai GZhu JDong ZXu JWen JBaeza-Yates RBonchi F(2024)Counteracting Duration Bias in Video Recommendation via Counterfactual Watch TimeProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671817(4455-4466)Online publication date: 25-Aug-2024
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Recommendations

A framework for diversifying recommendation lists by user interest expansion

A Scalable, Accurate Hybrid Recommender System

New Recommendation Techniques for Multicriteria Rating Systems