research-article

Boosting video popularity through recommendation systems

Authors:

Renjie Zhou,

Samamon Khemmarat,

Lixin Gao,

Huiqiang WangAuthors Info & Claims

DBSocial '11: Databases and Social Networks

Pages 13 - 18

https://doi.org/10.1145/1996413.1996416

Published: 12 June 2011 Publication History

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Abstract

While search engines are the major sources of content discovery on online content providers and e-commerce sites, their capability is limited since textual descriptions cannot fully describe the semantic of content such as videos. Recommendation systems are now widely used in online content providers and e-commerce sites and play an important role in discovering content. In this paper, we describe how one can boost the popularity of a video through the recommendation system in YouTube. We present a model that captures the view propagation between videos through the recommendation linkage and quantifies the influence that a video has on the popularity of another video. Furthermore, we identify that the similarity in titles and tags is an important factor in forming the recommendation linkage between videos. This suggests that one can manipulate the metadata of a video to boost its popularity.

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Cited By

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Choi MMolisch AHan DKim DKim JMoon J(2021)Probabilistic Caching and Dynamic Delivery Policies for Categorized Contents and Consecutive User DemandsIEEE Transactions on Wireless Communications10.1109/TWC.2020.304407620:4(2685-2699)Online publication date: Apr-2021
https://doi.org/10.1109/TWC.2020.3044076
Ferracani APezzatini DBertini MDel Bimbo AKender JSmith JLuo JBoll SHsu W(2016)Item-Based Video RecommendationProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912066(351-354)Online publication date: 6-Jun-2016
https://dl.acm.org/doi/10.1145/2911996.2912066
Zhou RKhemmarat SGao LWan JZhang JYin YYu J(2016)Boosting video popularity through keyword suggestion and recommendation systemsNeurocomputing10.1016/j.neucom.2016.05.002205:C(529-541)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.05.002
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Index Terms

Boosting video popularity through recommendation systems
1. Information systems
  1. Information systems applications

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Information & Contributors

Information

Published In

DBSocial '11: Databases and Social Networks

June 2011

41 pages

ISBN:9781450306508

DOI:10.1145/1996413

Conference Chairs:
Denilson Barbosa
University of Alberta
,
Gerome Miklau
University of Massachusetts, Amherst
,
Cong Yu
Google Research, New York

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 June 2011

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Research-article

Funding Sources

Division of Computer and Network Systems

Conference

SIGMOD/PODS '11

Sponsor:

SIGMOD

SIGMOD/PODS '11: International Conference on Management of Data

June 12 - 16, 2011

Athens, Greece

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Overall Acceptance Rate 9 of 19 submissions, 47%

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Cited By

View all

Choi MMolisch AHan DKim DKim JMoon J(2021)Probabilistic Caching and Dynamic Delivery Policies for Categorized Contents and Consecutive User DemandsIEEE Transactions on Wireless Communications10.1109/TWC.2020.304407620:4(2685-2699)Online publication date: Apr-2021
https://doi.org/10.1109/TWC.2020.3044076
Ferracani APezzatini DBertini MDel Bimbo AKender JSmith JLuo JBoll SHsu W(2016)Item-Based Video RecommendationProceedings of the 2016 ACM on International Conference on Multimedia Retrieval10.1145/2911996.2912066(351-354)Online publication date: 6-Jun-2016
https://dl.acm.org/doi/10.1145/2911996.2912066
Zhou RKhemmarat SGao LWan JZhang JYin YYu J(2016)Boosting video popularity through keyword suggestion and recommendation systemsNeurocomputing10.1016/j.neucom.2016.05.002205:C(529-541)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.05.002
Krishnappa DZink MGriwodz CHalvorsen P(2015)Cache-Centric Video RecommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/271631011:4(1-20)Online publication date: 2-Jun-2015
https://dl.acm.org/doi/10.1145/2716310
Tatar Ade Amorim MFdida SAntoniadis P(2014)A survey on predicting the popularity of web contentJournal of Internet Services and Applications10.1186/s13174-014-0008-y5:1Online publication date: 13-Aug-2014
https://doi.org/10.1186/s13174-014-0008-y
Santos-Neto EPontes TAlmeida JRipeanu MFerres LRossi GAlmeida VHerder E(2014)On the choice of data sources to improve content discoverability via textual feature optimizationProceedings of the 25th ACM conference on Hypertext and social media10.1145/2631775.2631815(273-278)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1145/2631775.2631815
Krishnappa DZink MGriwodz CHalvorsen PGriwodz C(2013)Cache-centric video recommendationProceedings of the 4th ACM Multimedia Systems Conference10.1145/2483977.2484008(261-270)Online publication date: 28-Feb-2013
https://dl.acm.org/doi/10.1145/2483977.2484008
Krishnappa DZink MGriwodz CBöszörmenyi LHalvorsen P(2013)What should you cache?Proceeding of the 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video10.1145/2460782.2460788(31-36)Online publication date: 26-Feb-2013
https://dl.acm.org/doi/10.1145/2460782.2460788

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