skip to main content
10.1145/1291233.1291467acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
Article

VideoSense: towards effective online video advertising

Published: 29 September 2007 Publication History

Abstract

With Internet delivery of video content surging to an unprecedented level, online video advertising is becoming increasingly pervasive. In this paper, we present a novel advertising system for online video service called VideoSense, which automatically associates the most relevant video ads with online videos and seamlessly inserts the ads at the most appropriate positions within each individual video. Unlike most current video-oriented sites that only display a video ad at the beginning or the end of a video, VideoSense aims to embed more contextually relevant ads at less intrusive positions within the video stream. Given an online video, VideoSense is able to detect a set of candidate ad insertion points based on content discontinuity and attractiveness, select a list of relevant candidate ads ranked according to global textual relevance, and compute local visual-aural relevance between each pair of insertion points and ads. To support contextually relevant and less intrusive advertising, the ads are expected to be inserted at the positions with highest discontinuity and lowest attractiveness, while the overall global and local relevance is maximized. We formulate this task as a nonlinear 0-1 integer programming problem and embed these rules as constraints. The experiments have proved the effectiveness of VideoSense for online video advertising.

References

[1]
AdWords. http://adwords.google.com/.
[2]
R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999.
[3]
S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
[4]
J.-C. Chen, W.-T. Chu, J.-H. Kuo, C.-Y. Weng, and J.-L. Wu. Tiling slideshow. In Proceedings of ACM Multimedia, 2006.
[5]
ComScore. http://www.comscore.com/.
[6]
L.-Y. Duan, J. Wang, Y. Zheng, J. S. Jin, H. Lu, and C. Xu. Segmentation, categorization, and identification of commercials from TV streams using multimodal analysis. In Proceedings of ACM Multimedia, pages 201--210, 2006.
[7]
eMarketer. http://www.emarketer.com/.
[8]
Google Video. http://video.google.com/.
[9]
X.-S. Hua, L. Lu, and H.-J. Zhang. Optimization-based automated home video editing system. IEEE Trans. on Circuit and Syst. for Video Tech., 14(5):572--583, 2004.
[10]
X.-S. Hua, T. Mei, W. Lai, and et al. Microsoft Research Asia TRECVID 2006 high-level feature extraction and rushes exploitation. In TREC Video Retrieval Evaluation Online Proceedings, 2006.
[11]
iTVx. http://www.itvx.com/.
[12]
G. Kastidou and R. Cohen. An approach for delivering personalized ads in interactive TV customized to both users and advertisers. In Proceedings of European Conference on Interactive Television, 2006.
[13]
P. Kim. Advertisers face TV reality. Forester Research, 2006.
[14]
A. Lacerda, M. Cristo, M. A. Goncalves, and et al. Learning to advertise. In Proceedings of ACM SIGIR, 2006.
[15]
G. Lekakos, D. Papakiriakopoulos, and K. Chorianopoulos. An integrated approach to interactive and personalized TV advertising. In Proceedings of Workshop on Personalization in Future TV, 2001.
[16]
H. Li, S. M. Edwards, and J.-H. Lee. Measuring the intrusiveness of advertisements: scale development and validation. Journal of Advertising, 31(2):37--47, 2002.
[17]
Y. Li, K. Wan, X. Yan, and C. Xu. Advertisement insertion in baseball video based on advertisement effect. In Proceedings of ACM Multimedia, pages 343--346, 2005.
[18]
Y.-F. Ma, L. Lu, H.-J. Zhang, and M. Li. A user attention model for video summarization. In Proceedings of ACM Multimedia, pages 533--542, 2002.
[19]
Y.-F. Ma and H.-J. Zhang. Contrast-based image attention analysis by using fuzzy growing. In Proceedings of ACM Multimedia, pages 374--381, Nov 2003.
[20]
S. Mccoy, A. Everard, P. Polak, and D. F. Galletta. The effects of online advertising. Communications of The ACM, 50(3):84--88, 2007.
[21]
Metacafe. http://www.metacafe.com/.
[22]
Online Publishers. http://www.online-publishers.org/.
[23]
Z. Rasheed and M. Shah. Detection and representation of scenes in videos. IEEE Trans. on Multimedia, 7(6):1097--1105, Dec. 2005.
[24]
Revver. http://one.revver.com/revver.
[25]
B. Ribeiro-Neto, M. Cristo, P. B. Golgher, and E. S. Moura. Impedance coupling in content-targeted advertising. In Proceedings of ACM SIGIR, 2005.
[26]
C. Rohrer and J. Boyd. The rise of intrusive online advertising and the response of user experience research at Yahoo! In Proceedings of ACM SIGCHI, 2004.
[27]
A. Thawani, S. Gopalan, and V. Sridhar. Context aware personalized ad insertion in an interactive TV environment. In Proceedings of Workshop on Personalization in Future TV, 2004.
[28]
TRECVID http://www-nlpir.nist.gov/projects/trecvid/.
[29]
K. Wan, X. Yan, X. Yu, and C. Xu. Robust goal-mouth detection for virtual content insertion. In Proceedings of ACM Multimedia, pages 468--469, Nov 2003.
[30]
D. Whitley. A genetic algorithm tutorial. Statistics and Computing, 4:65--85, 1994.
[31]
Yahoo! Video. http://video.yahoo.com/.
[32]
Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of SIGIR, 1999.
[33]
W.-T. Yih, J. Goodman, and V. R. Carvalho. Finding advertising keywords on web pages. In Proceedings of International World Wide Web Conference, 2006.
[34]
YouTube. http://www.youtube.com/.
[35]
H.-J. Zhang, A. Kankanhalli, and S. W. Smoliar. Automatic partitioning of full-motion video. Multimedia Systems, 1(1):10--28, June 1993.
[36]
L. Zhao, W. Qi, Y.-J. Wang, S.-Q. Yang, and H.-J. Zhang. Video shot grouping using best first model merging. In Proceedings of Storage and Retrieval for Media Database, pages 262--269, 2001.

Cited By

View all
  • (2025)Semantic Browsing & Advertising in YouTubeSmart Multimedia10.1007/978-3-031-82475-3_4(47-61)Online publication date: 4-Mar-2025
  • (2024)YAYIN İÇİ VİDEO REKLAMLARINA YÖNELİK DÜŞÜNCELER AÇISINDAN Y VE Z KUŞAKLARI ARASINDAKİ FARKLILIKLARNişantaşı Üniversitesi Sosyal Bilimler Dergisi10.52122/nisantasisbd.146918912:2(457-474)Online publication date: 31-Dec-2024
  • (2024)Automatic Generation of Interactive Nonlinear Video for Online Apparel Shopping NavigationIEEE Transactions on Multimedia10.1109/TMM.2023.326661526(474-486)Online publication date: 1-Jan-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
MM '07: Proceedings of the 15th ACM international conference on Multimedia
September 2007
1115 pages
ISBN:9781595937025
DOI:10.1145/1291233
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 September 2007

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. contextual relevance
  2. less intrusiveness
  3. online video advertising

Qualifiers

  • Article

Conference

MM07

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)158
  • Downloads (Last 6 weeks)16
Reflects downloads up to 28 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Semantic Browsing & Advertising in YouTubeSmart Multimedia10.1007/978-3-031-82475-3_4(47-61)Online publication date: 4-Mar-2025
  • (2024)YAYIN İÇİ VİDEO REKLAMLARINA YÖNELİK DÜŞÜNCELER AÇISINDAN Y VE Z KUŞAKLARI ARASINDAKİ FARKLILIKLARNişantaşı Üniversitesi Sosyal Bilimler Dergisi10.52122/nisantasisbd.146918912:2(457-474)Online publication date: 31-Dec-2024
  • (2024)Automatic Generation of Interactive Nonlinear Video for Online Apparel Shopping NavigationIEEE Transactions on Multimedia10.1109/TMM.2023.326661526(474-486)Online publication date: 1-Jan-2024
  • (2023)Does complementary role matter? An empirical study on paid search and social ads on purchaseSouth African Journal of Business Management10.4102/sajbm.v54i1.347254:1Online publication date: 28-Apr-2023
  • (2023)What Modality Matters? Exploiting Highly Relevant Features for Video Advertisement Insertion2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222693(3344-3348)Online publication date: 8-Oct-2023
  • (2022)Revenue and User Traffic Maximization in Mobile Short-Video AdvertisingProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3535972(1092-1100)Online publication date: 9-May-2022
  • (2022)REKLAMI TIKLA YA DA ATLA: TÜKETİCİLERİN ÇEVRİMİÇİ VİDEO İÇİ REKLAMLARI İZLEME DAVRANIŞLARI ÜZERİNE BİR ARAŞTIRMAPamukkale University Journal of Social Sciences Institute10.30794/pausbed.1094618Online publication date: 10-Jun-2022
  • (2022)SmartShots: An Optimization Approach for Generating Videos with Data Visualizations EmbeddedACM Transactions on Interactive Intelligent Systems10.1145/348450612:1(1-21)Online publication date: 4-Mar-2022
  • (2022)An Intelligent Advertisement Short Video Production System via Multi-Modal RetrievalProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3536323(3368-3372)Online publication date: 6-Jul-2022
  • (2022)LogoDet-3K: A Large-scale Image Dataset for Logo DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/346678018:1(1-19)Online publication date: 27-Jan-2022
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media