TY - GEN
T1 - A practical framework of conversion rate prediction for online display advertising
AU - Lu, Quan
AU - Pan, Shengjun
AU - Wang, Liang
AU - Pan, Junwei
AU - Wan, Fengdan
AU - Yang, Hongxia
N1 - Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
PY - 2017/8/14
Y1 - 2017/8/14
N2 - Cost-per-action (CPA), or cost-per-acquisition, has become the primary campaign performance objective in online advertising industry. As a result, accurate conversion rate (CVR) prediction is crucial for any real-time bidding (RTB) platform. However, CVR prediction is quite challenging due to several factors, including extremely sparse conversions, delayed feedback, attribution gaps between the platform and the third party, etc. In order to tackle these challenges, we proposed a practical framework that has been successfully deployed on Yahoo! BrightRoll, one of the largest RTB ad buying platforms. In this paper, we first show that over-prediction and the resulted over-bidding are fundamental challenges for CPA campaigns in a real RTB environment. We then propose a safe prediction framework with conversion attribution adjustment to handle over-predictions and to further alleviate over-bidding at different levels. At last, we illustrate both offline and online experimental results to demonstrate the effectiveness of the framework.
AB - Cost-per-action (CPA), or cost-per-acquisition, has become the primary campaign performance objective in online advertising industry. As a result, accurate conversion rate (CVR) prediction is crucial for any real-time bidding (RTB) platform. However, CVR prediction is quite challenging due to several factors, including extremely sparse conversions, delayed feedback, attribution gaps between the platform and the third party, etc. In order to tackle these challenges, we proposed a practical framework that has been successfully deployed on Yahoo! BrightRoll, one of the largest RTB ad buying platforms. In this paper, we first show that over-prediction and the resulted over-bidding are fundamental challenges for CPA campaigns in a real RTB environment. We then propose a safe prediction framework with conversion attribution adjustment to handle over-predictions and to further alleviate over-bidding at different levels. At last, we illustrate both offline and online experimental results to demonstrate the effectiveness of the framework.
UR - https://www.scopus.com/pages/publications/85058319920
U2 - 10.1145/3124749.3124750
DO - 10.1145/3124749.3124750
M3 - Conference article published in proceeding or book
AN - SCOPUS:85058319920
T3 - 2017 AdKDD and TargetAd - In conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2017
BT - 2017 AdKDD and TargetAd - In conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2017
PB - Association for Computing Machinery, Inc
T2 - AdKDD and TargetAd Workshop 2017
Y2 - 14 August 2017
ER -