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ADKDD '13: Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
ACM2013 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Chicago Illinois 11 August 2013
ISBN:
978-1-4503-2323-9
Published:
11 August 2013
Sponsors:
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Abstract

Online advertising is a key component in the whole internet ecosystem and is growing rapidly with constantly evolving business models and practices. Examples of online advertising include sponsored search, display advertising, Rich Media Ads, interstitial ads, online classified advertising, e-mail marketing and so on. With the rapid growth of social network, social network advertising (as exemplified by Facebook and Groupon) is taking off and playing an important role in the whole landscape. Also, more and more offline ads for both big brands and local businesses are moving online.

The online advertising industry is facing tons of challenges. For example, how to understand end users' need and advertisers' goals; what is the right strategy to connect user and ads; what ads should be delivered through which type of of advertising. These challenges bring great opportunities for researchers and data miners to come up with new technologies. Therefore, a forum for researchers and industry practitioners to exchange latest research results and construct collaborations will be of great service to the data mining community and generate value for the industry.

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research-article
Real time bid optimization with smooth budget delivery in online advertising
Article No.: 1, Pages 1–9https://doi.org/10.1145/2501040.2501979

Today, billions of display ad impressions are purchased on a daily basis through a public auction hosted by real time bidding (RTB) exchanges. A decision has to be made for advertisers to submit a bid for each selected RTB ad request in milliseconds. ...

research-article
TSum: fast, principled table summarization
Article No.: 2, Pages 1–9https://doi.org/10.1145/2501040.2501981

Given a table where rows correspond to records and columns correspond to attributes, we want to find a small number of patterns that succinctly summarize the dataset. For example, given a set of patient records with several attributes each, how can we ...

research-article
Real-time bidding for online advertising: measurement and analysis
Article No.: 3, Pages 1–8https://doi.org/10.1145/2501040.2501980

The real-time bidding (RTB), aka programmatic buying, has recently become the fastest growing area in online advertising. Instead of bulking buying and inventory-centric buying, RTB mimics stock exchanges and utilises computer algorithms to ...

research-article
CTR prediction for contextual advertising: learning-to-rank approach
Article No.: 4, Pages 1–8https://doi.org/10.1145/2501040.2501978

Contextual advertising is a textual advertising displayed within the content of a generic web page. Predicting the probability that users will click on ads plays a crucial role in contextual advertising because it influences ranking, filtering, ...

research-article
Audience segment expansion using distributed in-database k-means clustering
Article No.: 5, Pages 1–9https://doi.org/10.1145/2501040.2501982

Online display advertisers extensively use the concept of a user segment to cluster users into targetable groups. When the sizes of such segments are less than the desired value for campaign budgets, there is a need to use probabilistic modeling to ...

Contributors
  • University of Louisville
  • Microsoft Corporation

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Acceptance Rates

Overall Acceptance Rate 12 of 21 submissions, 57%
YearSubmittedAcceptedRate
ADKDD'17211257%
Overall211257%