skip to main content
research-article

Data mining and audience intelligence for advertising

Published: 01 December 2007 Publication History

Abstract

Growth in the global advertising industry - especially the recent rapid growth in online advertising - has generated large volumes of data, bringing along with it many challenging data mining problems. Researchers from various disciplines have brought their expertise to solve these exciting problems, leading to a plethora of novel applications and new algorithms. We strongly felt that we needed a forum where data mining researchers and practitioners, from both academia and the industry, could come together to share their experience on advertising. To this end, we organized ADKDD 2007 1, the First International Workshop on Data Mining and Audience Intelligence for Advertising, in conjunction with KDD 2007 at San Jose, California, USA. In this report, we will present a summary of the workshop.

References

[1]
G. Agarwal, K. Hosnagar, D. Pennock, M. Schwarz, and R. Vohra, Third Workshop on Sponsored Search Auctions, Banff, Canada, 2007.
[2]
M. Banko, M. J Cafarella, S. Soderland, M. Broad-head, and O. Etzioni, "Open Information Extraction from the Web," in IJCAI '07, Hyderabad, India, 2007.
[3]
N. Archak, A. Ghose, and P. Ipeirotis, "Show me the money: Deriving the Pricing Power of Product Features by Mining Consumer Reviews", KDD '07, San Jose, CA, 2007.
[4]
A. Broder, M. Fontoura, V. Josifovski, and L. Riedel. A semantic approach to contextual advertising. In SIGIR '07 pp. 559--566, New York, NY, USA, 2007.
[5]
J. J. Carrasco, D. Fain, K. Lang, and L. Zhukov. Clustering of bipartite advertiser-keyword graphs. Workshop on Large Scale Clustering at IEEE International Conference on Data Mining, 2003.
[6]
H. Dai, L. Zhao, Z. Nie, J.-R. Wen, L. Wang and Y. Li, Detecting Online Commercial Intention, WWW '06, Edinburgh, Scotland, 2006.
[7]
K. Fujimura, T. Inoue, and M. Sugisaki. The eigenrumor algorithm for ranking blogs. WWW 2005 2nd Annual Workshop on the Weblogging Ecosystem, 2005.
[8]
"Google Announces TV Ads Trial", http://www.google.com/intl/en/press/annc/tv_ads_trial.html
[9]
J. Hu, H-J., Zeng, H. Li, C. Niu, and Z. Chen, Demographic Prediction based on User's Browsing Behavior, WWW '07, Banff, Canada.
[10]
B. Huberman and F. Wu. The economics of attention: Maximizing user value in information-rich environments. ADKDD '07 San Jose, CA, USA, 2007.
[11]
X. Jin, Y. Li, T. Mah, and J. Tong. Sensitive webpage classification for content advertising. ADKDD '07 San Jose, CA, USA, 2007.
[12]
A. Joshi and R. Motwani. Keyword generation for search engine advertising. ICDMW '06: Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops, pp. 490--496, Washington, DC, USA, 2006.
[13]
R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. WWW '03, pages 568--576, New York, NY, USA. 2003.
[14]
A. Lacerda, M. Cristo, M. A. Gonçalves, W. Fan, N. Ziviani, and B. Ribeiro-Neto. Learning to advertise. SIGIR '06 pp. 549--556, New York, NY, USA, 2006.
[15]
M. Mahdian and K. Tomak. Pay-per-action model for online advertising. ADKDD'07 San Jose, CA, USA, 2007.
[16]
C. D. Manning, P. Raghavan, and H. SchÜZe, Introduction To Information Retrieval. Cambridge University Press, 2007.
[17]
Q. Mei, C. Liu, H. Su, and C. Zhai. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. WWW '06, Edinburgh, Scotland, 2006.
[18]
V. Murdock, M. Ciaramita, and V. Plachouras. A noisy channel approach to contextual advertising. ADKDD'07 San Jose, CA, USA, 2007.
[19]
D. Pregibon and D. Lambert. More bang for their bucks: Assessing new features for online advertisers. ADKDD '07 San Jose, CA, USA, 2007.
[20]
"Global Entertainment and Media Outlook: 2007--2011", PriceWaterhouseCoopers Report, January 2007
[21]
G. Qiu, K. Liu, C. C. Jiajun Bu, and Z. Kang. Extracting opinion topics for chinese opinions using dependence grammar. ADKDD '07 San Jose, CA, USA, 2007.
[22]
Y. Qiu, H.-P. Frei, Concept based query expansion, SIGIR '93 pp. 160--169, Pittsburgh, PA, 1993.
[23]
S. Raaijmakers. Sentiment classification with interpolated information diffusion kernels. ADKDD '07 San Jose, CA, USA, 2007.
[24]
M. Richardson, E. Dominowska, and R. Ragno Predicting Clicks: Estimating Click-through Rate for New Ads WWW '07, Banff, Canada, 2007.
[25]
D. Shen, J.-T. Sun, Q. Yang, and Z. Chen, Building bridges for web query classification, SIGIR '06 pp. 131--138, Seattle, WA, 2006.
[26]
A. Stewart, L. Chen, R. Paiu, and W. Nejdl. Discovering information diffusion paths from blogosphere for online advertising. ADKDD '07 San Jose, CA, USA, 2007.
[27]
TSN Media Intelligence Report, 2007, http://www.tns-mi.com/news/01082007.htm
[28]
"Americans and Online Privacy: The System is Broken", A Report from the Annenberg Public Policy Center of the University of Pennylvania, by Joseph Turow, 2003.
[29]
"Open to Exploitation: American Shoppers Online and Offline," Report of the Annenberg Public Policy Center, by Joseph Turow, June 2005.
[30]
W. Yih, J. Goodman and V. R. Carvalho, Finding Advertising Keywords on Web Pages, WWW '06, Edinburgh, Scotland, 2006.

Cited By

View all

Comments

Information & Contributors

Information

Published In

cover image ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter  Volume 9, Issue 2
Special issue on visual analytics
December 2007
105 pages
ISSN:1931-0145
EISSN:1931-0153
DOI:10.1145/1345448
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2007
Published in SIGKDD Volume 9, Issue 2

Check for updates

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)6
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all

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