Call for Workshop Papers
Today the average consumer spends 8+ hours a day across all devices interacting with online content almost entirely sponsored by advertisements. At over $550B global market size in 2022, and expected to pass $800B by 2026, digital advertising has already surpassed traditional ads in global spend and continues to grow despite economic headwinds. Digital advertising and in particular computational advertising is perhaps the most visible and ubiquitous application of machine learning and one that interacts directly with consumers. When done right, ads connect us to opportunities to enrich our lives and creep us out when done badly. Recently at the forefront of political battles between governments, large multinational corporations, and consumers, digital advertising remains a dynamic industry and research area.
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 from simple rule-based ads controlled by monolithic publishers and randomly rotating banner ads to highly personalized content experiences within native, video, and display formats on mobile devices, connected TV, and audio—all utilizing data amassed from petabytes of stored user data collected increasingly through inferred identity providers. Increasing use of sequence models and the rise of Generative AI is spawning new directions such as auto-generation of ad creatives or pay-for-use shopping assistants. Ads are far from done.
The AdKDD workshops have had a lot of interest and success in the past years. A total of sixteen 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 in-person 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 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 uses of Generative AI and LLMs in Advertising: Given all the buzz around GPTs and LLMs, we particularly welcome submissions on applications of these technologies to online advertising. Focus on the infra needed, computational cost and how these systems are harnessed to meet the millisecond latencies that are common in advertising is encouraged.
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.
All accepted papers will be eligible to be published in the ACM Digital Library and will be archived on the AdKDD website.