Feedback Control of Real-Time Bidding Advertising

Transcription

1 Feedback Control of Real-Time Bidding Advertising Fatemeh Gheshlaghpour Advisors: Maryam Babazadeh Amin Nobakhti Sharif University of Technology, EE Department Apr. 2018

2 Overview Introduction Business Model of the RTB Markets Key Roles in RTB Market The Business Process of RTB Ad Delivery Second-Price Sealed-Bid Auction Key Research Issues in the RTB IPinYou Dataset Basic Statistics User Feedback RTB Feedback Control System Bidding Strategy Logistic Estimator Actuator Control Issues of the Problem Reference and Feedback Signals Model Results 2/31

3 Introduction In the previous generations of the display advertising the advertiser paid in two different platforms: CPM (cost per mille) -> do not getting satisfactory numbers of clicks. CPC (cost per click) -> being subjected to click frauds. Internet users, on the other hand, faced with lots of irrelevant advertisements on their screens -> Ad-Blockers! 3/31

4 Introduction The Solution was the RTB in which the advertisers should bid for each impression based on some behavioral and contextual data. The business process, including audience identification, auction and ad display, will be finished in exactly 10 to 100 milliseconds, and hence it is named "real-time bidding". A basic problem for RTB bidding agents is to figure out how much to bid for an incoming bid request. 4/31

5 Business Model of the RTB Markets The key roles in RTB markets Advertiser Demand side platform (DSP) is a platform that helps advertisers optimize their strategies. Ad exchange (AdX) is an ad exchange market that matches the buyers and sellers. Supply side platform (SSP) is a platform that helps publishers optimize the strategies. Data management platform (DMP) is a platform that analyzes the cookie data of Internet users. Publisher 5/31

6 Business Model of the RTB Markets The Business Process of RTB Ad Delivery Myerson has proved that its optimal mechanism is second-price sealed-bid auction. The business process of RTB ad delivery.* *Yuan, Y., Wang, F., Li, J., & Qin, R. (2014). A survey on real time bidding advertising. Proceedings of the IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Qingdao, China (pp ). 6/31

7 Key Research Issues in the RTB Inventory Pricing and Channel Allocation In RTB markets, publishers and SSPs constitute the supply side of ad resources. Their key decisions, such as inventory pricing and multi-channel allocation of ad impressions, are major re search topics in literatures. Business Model and Mechanism Design Similarly working like the stock markets, AdX can bridge the gap in RTB markets by matching advertisers to publishers via real-time auctions. The existing works are focused on the design of the business model and auction mechanisms of AdXs and DSPs. 7/31

8 Key Research Issues in the RTB Market Segmentation and Ad Performance Analysis Via designing the audience classification category and attribute labels, DSPs can divide the Internet users into large amounts of niche markets with different kinds of demographic characteristics or shopping interests, and display best-matched ads accordingly. Bidding Behavior Analysis and Strategy Optimization In RTB markets, advertisers and DSPs constitute the demand side of ad resources, seeking to buy best-matched ad impressions via realtime auctioning and bidding. In literatures, bidding behavior analysis and strategy optimization for advertisers and DSPs attract intensive interests. 8/31

9 Second-Price Sealed-Bid Auction 9/31

10 Second-Price Sealed-Bid Auction Nash Equilibrium: The only NE which does not need to know other players private values is to bid truthfully, i.e. to bid same as the private value. 10/31

11 IPinYou Dataset Fortunately, a leading Chinese advertising technology company ipinyou decided to release the dataset used in its global RTB algorithm competition in The dataset includes logs of ad auctions, bids, impressions, clicks, and final conversions. These logs reflect the market environment as well as form a complete path of users responses from advertisers perspective. 11/31

12 The ipinyou data format.* *Zhang, W., Yuan, S., Wang, J., & Shen X. (2014). Real-time bidding benchmarking with ipinyou dataset. arxiv: /31

13 Basic Statistics 13/31

14 User Feedback Some statistics of user feedback on campaigns 1458 and 3358 are shown bellow. 14/31

15 User Feedback 15/31

16 User Feedback 16/31

17 RTB Feedback Control System The traditional bidding strategy is represented as the bid calculator module in the DSP bidding agent. The controller plays as a role which adjusts the bid price from the bid calculator. Feedback controller integrated in the RTB system.* *Zhang, W., Yuan, S., & Wang, J. (2014). Optimal real-time bidding for display advertising. Proceedings of the 20 th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), New York City, NY, USA (pp ). 17/31

18 Bidding Strategy t b( t) = b θ 0 θ0 18/31

19 Logistic Estimator Logistic estimator predicts the CTR (a real value between 0 and 1) of an ad given a set of features. Where f i (ad) is the value of the i th feature for the ad, and w i is the learned weight for that feature. 19/31

20 Logistic Estimator In our experiment, all the features for LR are binary. The weekday and hour feature are extracted from timestamps. The floor price is processed by buckets of 0, [1,10], [11,50], [51,100] and [101,+ ). We do not include the features of Bid ID, Log Type, ipinyou ID, URL, Anonymous URL ID, Bidding Price, Paying Price, Key Page URL. In sum, we have 937,748 binary features for LR training and prediction. 20/31

21 Logistic Estimator 600 Validation of LR for impressions with click= #ectr > 0.5 #ectr < Validation of LR for impressions with click= #ectr > 0.5 #ectr < /31

22 Actuator For example it can be chose to use: For instance in a PID controller we have: 22/31

23 Control Issues of the Problem Same as any other control problems we should take care of these: Reference Signal -> What is our controlling goal? Feedback Signal -> Do we have access to this signal? Model -> Can we have a static/dynamic model of the process? 23/31

24 Reference and Feedback Signals The advertisers are provided with the data of the impressions. So we can define a feedback signal. Reference signal is the output of an optimization problem. In order to have smooth budget delivery we should set another constraint, too. 24/31

25 Reference and Feedback Signals Illustration of different budget pacing schemes with respect to the portion of the budget spent every time interval.* *Lee, Kuang-Chih, Ali Jalali, and Ali Dasdan. "Real time bid optimization with smooth budget delivery in online advertising." Proceedings of the Seventh International Workshop on Data Mining for Online Advertising. ACM, /31

26 Reference and Feedback Signals Finally, we should optimize the following goal: 26/31

27 Model As the time-constant of the dynamic model is not short enough we can not have it using the data. 27/31

28 Results Here is the AWR signal for the campaign 2998 using impression-based PI controllers. 28/31

29 Results Bellow you can see the overall control performance on AWR for the campaign 2998 using some different methods. Performance of different AWR controllers Method ref #Ad #clk total_cost AWR CPC CPM CTR rise-time settling-time rmse-ss awr_rand_pi control_awr_pi awr_pi_constant awr_pi static_bid_awr_k /31

30 Results Bellow you can see the overall control performance on CPC for the campaign 2998 using some different methods. Performance of different CPC controllers Method ref #Ad #clk total_cost AWR CPC CPM CTR rise-time settling-time rmse-ss control_ecpc_pi cpc_constant_pi cpc_rand_pi /31

31 Thank you for your time 31/31