wnzhang/rtb-papers

This repository is a curated reading list of academic research papers about real-time bidding in online display advertising. Real-time bidding is the technology behind the automated auctions that happen in milliseconds when a webpage loads, deciding which advertiser gets to show an ad to a particular user and at what price. The repository contains no software, just a long organized list of links to papers. The list is maintained by Weinan Zhang, a researcher in computational advertising, and is shared for academic purposes. Papers are grouped by topic, covering areas such as predicting whether a user will click an ad, estimating conversion rates, setting bid prices optimally, allocating budgets across campaigns, auction design and mechanisms, and the technology infrastructure behind ad exchanges. There are also sections on supply-side platform techniques, user modeling, reinforcement learning approaches to bidding, and fraud detection. Each entry in the list includes the paper title as a link, the authors, the conference or journal where it was published, and the year. Papers span from around 2009 through the late 2010s and into the 2020s, with the bulk of entries from major machine learning and information retrieval conferences such as KDD, WWW, SIGIR, and IJCAI. A 2016 book-length treatment of the subject is listed at the top. Anyone can contribute by forking the repository and adding missing papers, or by contacting the maintainer directly. The repository serves as a starting point for researchers or practitioners who want an organized overview of the academic literature on this topic.