AdKdd + TargetAd 2018

Call for Papers

Important Dates

Description

The workshop focuses on three main aspects of computational advertising.

Evolution of computational advertising: Online advertising has progressed beyond the notion of traditional desktop ads to ads that are native, social, mobile, and contextual. In tandem, the rise of new mechanisms, such as header bidding, complex ad exchanges, repeated auctions, ad blockers, viewability trackers and others, challenge the traditional notions of advertising. There also continues to exists controversial issues in advertising such as privacy, security, fraud, ethics, and economic attribution. We invite papers that are focused on some of the above aspects.

Large-scale and novel ad targeting: Recent advances in real-time, bid data systems, and easier accessibility to different types of data make it possible to design more personalized and efficient ad targeting systems. We invite papers that advance the state-of-the-art in related areas of ad targeting.

Deployed systems & battle scars: We particularly encourage papers that highlight experience in deploying real-time ad targeting systems, data and audience insights, as well as position papers on the future of online advertising.

Submission Instructions

Following KDD conference tradition, reviews are single-blind, and author names and affiliations should be listed. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. For each accepted paper, at least one author must attend the workshop and present the paper.

Submissions are limited to a total of six pages, including all content and references, must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template . Additional information about formatting and style files is available here.

Submission Website

Call for Papers

Important Dates:

Submission

May 28, 2018

Decision

June 11, 2018

Camera-ready

July 6, 2018

Workshop

August 20, 2018

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Description

The workshop focuses on three main aspects of computational advertising.

Evolution of computational advertising: Online advertising has progressed beyond the notion of traditional desktop ads to ads that are native, social, mobile, and contextual. In tandem, the rise of new mechanisms, such as header bidding, complex ad exchanges, repeated auctions, ad blockers, viewability trackers and others, challenge the traditional notions of advertising. There also continues to exist controversial issues in advertising such as privacy, security, fraud, ethics, and economic attribution. We invite papers that are focused on the above aspects, and especially on emerging topics such as Ad Blockers and GDPR (General Data Protection Regulation).

Large-scale and novel ad targeting: Recent advances in real-time, big data systems, and easier accessibility to different types of data make it possible to design more personalized and efficient ad targeting systems. We invite papers that advance the state-of-the-art in related areas of ad targeting.

Deployed systems & battle scars: We particularly encourage papers that highlight experience in deploying real-time ad targeting systems, data and audience insights, as well as position papers on the future of online advertising.

Submission Instructions

Submissions are limited to a total of six pages, including all content and references, must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. Additional information about formatting and style files is available here.

Awards

We are thankful to OLX/Naspers, Criteo AI Lab and Jampp for supporting our workshop with the following awards:

Best Paper: $1,000 (sponsored by OLX & Criteo AI Lab)
Best Student Paper: $1,000 (sponsored by OLX & Criteo AI Lab)
Student Travel Award: $500 for upto two PhD Students (sponsored by OLX)
Keynote speaker recognition: $1000 (sponsored by Jampp)

Submission Website

Tim Roughgarden

Tim Roughgarden is a Professor in the Computer Science Department at Stanford University, which he joined in 2004 following a PhD at Cornell and a postdoc at UC Berkeley. He works on the boundary of computer science and economics, and on the design, analysis, applications, and limitations of algorithms.

Rukmini Iyer

Rukmini earned a PhD in EE from Boston University, and worked in the speech recognition and natural language processing space for almost 10 years at BBN Technologies and Nuance. Moving to Yahoo in 2005, she was enlisted into machine learning for search advertising – to improve online revenue and user experience. Rukmini joined Microsoft in 2010 to build prototypes of personal assistants using advanced conversational understanding, but moved back to search advertising in 2012.

Vahab Mirrokni

Vahab is a principal scientist (and technical research director), heading the algorithms research groups at Google Research in New York. His groups at Google Research New York include market algorithms, graph mining, and large-scale optimization groups. He also teaches Algorithms and Economics of the Internet as an adjunct associate professor at the Courant Institute at NYU.

Speakers

2018 AdKDD & TargetAd

in conjunction with

The 24rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018)

Looking for this year?

Visit http://www.adkdd.org

August 20, 2018

London, United Kingdom

An average consumer today spends 8+ hours across all devices, interacting with online content almost entirely sponsored by advertisements. At projected $300B market share by 2020, online advertising and computation advertising in particular is perhaps the most visible and ubiquitous application of machine learning, and one that interacts directly with consumers. When done right ads help us enrich our lives, and creep us out when done poorly.

Looking at the published literature over the last few years, many researchers might consider computational advertising as a mature field. Yet, quite the opposite is true. Computational advertising is constantly evolving, transforming from ads controlled by monolithic publishers and randomly rotating banner ads to highly personalized content experiences in new feeds on mobile devices and even on TV. Computational targeting systems are trained on petabytes of data amassed from thousands of hours of online user activities, with these numbers growing larger by the day. Targeted ads are far from done.

There has been a total of eleven AdKDD and TargetAd workshops to date, organized every year since 2007, which focused on highlighting state-of-the-art advances in computational advertising. All the workshops were well attended, often with standing room only, and very well received both by the academic community and the advertising industry. Motivated by these successes, for 2018 we are happy to announce a joint edition of AdKDD and TargetAd, which we believe will bring an even stronger program than the past years. We look forward to seeing you in London to discuss the past, present, and future of computational advertising!

Full day workshop. Schedule TBD.