Alert button
Picture for Shengjun Pan

Shengjun Pan

Alert button

An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

Jul 15, 2021
Tian Zhou, Hao He, Shengjun Pan, Niklas Karlsson, Bharatbhushan Shetty, Brendan Kitts, Djordje Gligorijevic, San Gultekin, Tingyu Mao, Junwei Pan, Jianlong Zhang, Aaron Flores

Figure 1 for An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Figure 2 for An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Figure 3 for An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Figure 4 for An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference between these auctions, demand-side platforms (DSPs) have had to update their bidding strategies to avoid bidding unnecessarily high and hence overpaying. Bid shading was proposed to adjust the bid price intended for second-price auctions, in order to balance cost and winning probability in a first-price auction setup. In this study, we introduce a novel deep distribution network for optimal bidding in both open (non-censored) and closed (censored) online first-price auctions. Offline and online A/B testing results show that our algorithm outperforms previous state-of-art algorithms in terms of both surplus and effective cost per action (eCPX) metrics. Furthermore, the algorithm is optimized in run-time and has been deployed into VerizonMedia DSP as production algorithm, serving hundreds of billions of bid requests per day. Online A/B test shows that advertiser's ROI are improved by +2.4%, +2.4%, and +8.6% for impression based (CPM), click based (CPC), and conversion based (CPA) campaigns respectively.

* In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), August 14-18, 2021, Singapore 
Viaarxiv icon

Bid Shading by Win-Rate Estimation and Surplus Maximization

Sep 19, 2020
Shengjun Pan, Brendan Kitts, Tian Zhou, Hao He, Bharatbhushan Shetty, Aaron Flores, Djordje Gligorijevic, Junwei Pan, Tingyu Mao, San Gultekin, Jianlong Zhang

Figure 1 for Bid Shading by Win-Rate Estimation and Surplus Maximization
Figure 2 for Bid Shading by Win-Rate Estimation and Surplus Maximization
Figure 3 for Bid Shading by Win-Rate Estimation and Surplus Maximization
Figure 4 for Bid Shading by Win-Rate Estimation and Surplus Maximization

This paper describes a new win-rate based bid shading algorithm (WR) that does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform (SSP). The method uses a modified logistic regression to predict the profit from each possible shaded bid price. The function form allows fast maximization at run-time, a key requirement for Real-Time Bidding (RTB) systems. We report production results from this method along with several other algorithms. We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost. Further, the particular approach described in this paper captures 7% more profit for advertisers, than do benchmark methods of just bidding the most probable winning price. We also report 4.3% higher surplus than an industry Sell-Side Platform shading service. Furthermore, we observed 3% - 7% lower eCPM, eCPC and eCPA when the algorithm was integrated with budget controllers. We attribute the gains above as being mainly due to the explicit maximization of the surplus function, and note that other algorithms can take advantage of this same approach.

* AdKDD 2020 
Viaarxiv icon

Bid Shading in The Brave New World of First-Price Auctions

Sep 02, 2020
Djordje Gligorijevic, Tian Zhou, Bharatbhushan Shetty, Brendan Kitts, Shengjun Pan, Junwei Pan, Aaron Flores

Figure 1 for Bid Shading in The Brave New World of First-Price Auctions
Figure 2 for Bid Shading in The Brave New World of First-Price Auctions
Figure 3 for Bid Shading in The Brave New World of First-Price Auctions
Figure 4 for Bid Shading in The Brave New World of First-Price Auctions

Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth. With great changes in how auctions are being organized, such as changing the second- to first-price auction type, advertisers and demand platforms are compelled to adapt to a new volatile environment. Bid shading is a known technique for preventing overpaying in auction systems that can help maintain the strategy equilibrium in first-price auctions, tackling one of its greatest drawbacks. In this study, we propose a machine learning approach of modeling optimal bid shading for non-censored online first-price ad auctions. We clearly motivate the approach and extensively evaluate it in both offline and online settings on a major demand side platform. The results demonstrate the superiority and robustness of the new approach as compared to the existing approaches across a range of performance metrics.

* In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM'20), October 19-23, 2020, Virtual Event, Ireland 
Viaarxiv icon

Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

Jun 09, 2018
Junwei Pan, Jian Xu, Alfonso Lobos Ruiz, Wenliang Zhao, Shengjun Pan, Yu Sun, Quan Lu

Figure 1 for Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
Figure 2 for Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
Figure 3 for Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
Figure 4 for Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising

Click-through rate (CTR) prediction is a critical task in online display advertising. The data involved in CTR prediction are typically multi-field categorical data, i.e., every feature is categorical and belongs to one and only one field. One of the interesting characteristics of such data is that features from one field often interact differently with features from different other fields. Recently, Field-aware Factorization Machines (FFMs) have been among the best performing models for CTR prediction by explicitly modeling such difference. However, the number of parameters in FFMs is in the order of feature number times field number, which is unacceptable in the real-world production systems. In this paper, we propose Field-weighted Factorization Machines (FwFMs) to model the different feature interactions between different fields in a much more memory-efficient way. Our experimental evaluations show that FwFMs can achieve competitive prediction performance with only as few as 4% parameters of FFMs. When using the same number of parameters, FwFMs can bring 0.92% and 0.47% AUC lift over FFMs on two real CTR prediction data sets.

Viaarxiv icon