Call for Workshop Papers
AdKDD 2021 in conjunction with The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021) Virtual event , August 14th-18th, 2021 http://www.adkdd.org 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. 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! Topics: 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 some of the above aspects. 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: 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<http://www.acm.org/publications/proceedings-template>. All accepted papers will be eligible to be published in the ACM Digital Library and will be archived on the AdKDD website. Important Deadlines: Submission : May 25th, 2021 Decisions : June 10th, 2021 Camera-ready : June 20th, 2021 Video Submission: July 24, 2021 Workshop : August 14th, 2021 Best Paper Awards: We are happy to announce that we will award the best accepted papers for this year’s workshop. Details are to be disclosed shortly. ML Challenge (organized and sponsored by Criteo): Machine Learning Challenge on Aggregated, Differentially Private Data 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 differential privacy<https://en.wikipedia.org/wiki/Differential_privacy> mechanisms were shown to match high privacy standards but also raise concerns about the possibility to learn relevant machine learning models for ad placement. 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 several open datasets<https://ailab.criteo.com/ressources/> 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 Privacy Sandbox<https://www.chromium.org/Home/chromium-privacy/privacy-sandbox> discussed in the Improving Web Advertising<https://www.w3.org/groups/bg/web-adv> forum at W3C. Important info: * Competition website: https://competitions.codalab.org/competitions/31485 * Contact address: adkdd21challe...@googlegroups.com<mailto:adkdd21challe...@googlegroups.com> 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. Tentative timeline (anywhere on Earth): * Apr 10th: Competition announcement * May 1st: Final tasks description and evaluation metrics published * May 10th: Competition starts * July 31th: Competition ends * Workshop day: Winners present their solutions and prizes awarded Submission Website: https://easychair.org/conferences/?conf=adkdd2021 Program Committee Chairs: Abraham Bagherjeiran<https://www.linkedin.com/in/abagher> (eBay) Nemanja Djuric<https://djurikom.github.io/> (Aurora Innovation) Mihajlo Grbovic<http://astro.temple.edu/~tua95067/> (AirBnB) Kuang-chih Lee<http://vision.ucsd.edu/~leekc/> (Alibaba) Kun Liu<https://www.linkedin.com/in/kunliu1> (Amazon) Vladan Radosavljevic<https://www.linkedin.com/in/vladan-radosavljevic-69244265/> (Spotify) Suju Rajan<https://www.linkedin.com/in/suju-rajan-9b56581> (LinkedIn) For further questions please contact the organizers at organiz...@adkdd.org.
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