2017/11/20 by Kan Ren, Weinan Zhang, Ke Chang +3 · 79 citations
Business, Management and Accounting · Computer Science · Decision Sciences · #Artificial intelligence #Auction Theory and Applications #Bidding #Computer science #Consumer Market Behavior and Pricing #Display advertising #Ebidding #Economics #Image and Video Quality Assessment #Machine learning #Microeconomics #Online advertising #Operations research #Profit (economics) #Real-time bidding #The Internet #World Wide Web #cs.CY #cs.GT #cs.IR #cs.LG
paper · pdf · doi:10.1109/tkde.2017.2775228
published in IEEE Transactions on Knowledge and Data Engineering 30(4), 645-659 (IEEE Computer Society) · 18 pages, 10 figures, Final version published in IEEE Transactions on Knowledge and Data Engineering (TKDE), URL: http://ieeexplore.ieee.org/document/8115218/
openalex publication_date 2017/11/20 · arxiv created 2018/03/11 · arxiv updated 2018/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Real-time bidding (RTB) based display advertising has become one of the key technological advances in computational advertising. RTB enables advertisers to buy individual ad impressions via an auction in real-time and facilitates the evaluation and the bidding of individual impressions across multiple advertisers. In RTB, the advertisers face three main challenges when optimizing their bidding strategies, namely (i) estimating the utility (e.g., conversions, clicks) of the ad impression, (ii) forecasting the market value (thus the cost) of the given ad impression, and (iii) deciding the optimal bid for the given auction based on the first two. Previous solutions assume the first two are solved before addressing the bid optimization problem. However, these challenges are strongly correlated and dealing with any individual problem independently may not be globally optimal. In this paper, we propose Bidding Machine, a comprehensive learning to bid framework, which consists of three optimizers dealing with each challenge above, and as a whole, jointly optimizes these three parts. We show that such a joint optimization would largely increase the campaign effectiveness and the profit. From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes. Our extensive offline empirical study and online A/B testing verify the high effectiveness of the proposed bidding machine.