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<span style="font-size:13pt;font-family:Arial;color:#000000;font-weight:400">Call for Workshop Papers</span></p>
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<span style="font-size:13pt;font-family:Arial;color:#000000;font-weight:400">AdKDD 2021</span></p>
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<span style="font-size:12pt;font-family:Arial;color:#000000;font-weight:400">in conjunction with</span></p>
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<span style="font-size:13pt;font-family:Arial;color:#000000;font-weight:400">The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021)</span></p>
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<span style="font-size:13pt;font-family:Arial;color:#000000;font-weight:400">Virtual event , August 14th-18th, 2021</span></p>
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<span style="font-size:13pt;font-family:Arial;color:#000000;font-weight:400">http://www.adkdd.org</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">Today, the average consumer spends 8+ hours a day across all devices interacting with online content almost entirely sponsored by advertisements. At over $300B global market
size in 2021 and expected to pass $1T by 2027, online advertising has already surpassed traditional ads in global spend. Moreover, computational 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 badly. Looking at the published literature over the last few years, many researchers might consider computational advertising as a mature field. Yet,
the opposite is true. Computational advertising is evolving, however, 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—all utilizing data
amassed from petabytes of stored user data. Ads are far from done.</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">The AdKDD workshops have had a lot of interest and success in the past years. A total of fourteen workshops have been organized every year since 2007, focusing 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. We look forward to seeing you virtually to discuss
the past, present, and future of computational advertising!</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Topics:</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">The workshop focuses on three main aspects of computational advertising.</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:700">Evolution of computational advertising:</span><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> 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 some of the above aspects.</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:700">Large-scale and novel ad targeting:</span><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> 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.</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:700">Deployed systems & battle scars:</span><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> 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.</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> </span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Submission Instructions:</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">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. </span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">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 </span>
<a href="http://www.acm.org/publications/proceedings-template"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">here</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">.</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">All accepted papers will be eligible to be published in the ACM Digital Library and will be archived on the AdKDD website. </span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Important Deadlines:</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">Submission : May 25th, 2021</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">Decisions : June 10th, 2021</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">Camera-ready : June 20th, 2021</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">Video Submission: July 24, 2021</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">Workshop : August 14th, 2021</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Best Paper Awards:</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">We are happy to announce that we will award the best accepted papers for this year’s workshop. Details are to be disclosed shortly.</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">ML Challenge (organized and sponsored by Criteo):</span></p>
<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">Machine Learning Challenge on Aggregated, Differentially Private Data</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">The Online Advertising industry is seeing a major shift today in its operational constraints with a
global movement towards more privacy. Popular techniques for privacy-compliant advertising such as aggregation and
</span><a href="https://en.wikipedia.org/wiki/Differential_privacy"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">differential privacy</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> mechanisms
were shown to match high privacy standards but also raise concerns about the possibility to learn relevant machine learning models for ad placement.</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">We propose in this challenge to explore the trade-off between privacy level and prediction performance,
on data donated by Criteo - an industry leader that already released </span><a href="https://ailab.criteo.com/ressources/"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">several
open datasets</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> for research purposes. To anchor the competition in reality, the challenge design is inspired by (and as close as possible/convenient to) current
propositions in the </span><a href="https://www.chromium.org/Home/chromium-privacy/privacy-sandbox"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Privacy Sandbox</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> discussed
in the </span><a href="https://www.w3.org/groups/bg/web-adv"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Improving Web Advertising</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> forum
at W3C.</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span>Competition website:
</span><a href="https://competitions.codalab.org/competitions/31485"><span style="color:#1155cc;text-decoration:underline;text-decoration-skip-ink:none">https://competitions.codalab.org/competitions/31485</span></a></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span>Contact address:
</span><a href="mailto:adkdd21challenge@googlegroups.com"><span style="color:#1155cc;text-decoration:underline;text-decoration-skip-ink:none">adkdd21challenge@googlegroups.com</span></a></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">The top 3 winners of each task will share $20,000 of prize money that is sponsored by Criteo, and be
invited to present their solution to the workshop.</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span>Apr 10th: Competition announcement</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span>May 1st: Final tasks description and evaluation metrics published</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span>May 10th: Competition starts</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span>July 31th: Competition ends</span></p>
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<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span>Workshop day: Winners present their solutions and prizes awarded</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Submission Website:</span></p>
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<a href="https://easychair.org/conferences/?conf=adkdd2021"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">https://easychair.org/conferences/?conf=adkdd2021</span></a></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400;text-decoration:underline;text-decoration-skip-ink:none">Program Committee Chairs:</span></p>
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<a href="https://www.linkedin.com/in/abagher"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400">Abraham Bagherjeiran</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> (eBay)</span></p>
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<a href="https://djurikom.github.io/"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400">Nemanja Djuric</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> (Aurora Innovation)</span></p>
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<a href="http://astro.temple.edu/~tua95067/"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400">Mihajlo Grbovic</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> (AirBnB)</span></p>
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<a href="http://vision.ucsd.edu/~leekc/"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400">Kuang-chih Lee</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> (Alibaba)</span></p>
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<a href="https://www.linkedin.com/in/kunliu1"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400">Kun Liu</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> (Amazon)</span></p>
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<a href="https://www.linkedin.com/in/vladan-radosavljevic-69244265/"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400">Vladan Radosavljevic</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> (Spotify)</span></p>
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<a href="https://www.linkedin.com/in/suju-rajan-9b56581"><span style="font-size:11.5pt;font-family:Cambria,serif;color:#1155cc;font-weight:400">Suju Rajan</span></a><span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400"> (LinkedIn)</span></p>
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<span style="font-size:11.5pt;font-family:Cambria,serif;color:#000000;font-weight:400">For further questions please contact the organizers at organizers@adkdd.org.</span></p>
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