Building a Dynamic Bidding system for a location based Display advertising Platform

1. Experimentation to Productization of a Location based Dynamic Bidding system Ekta Grover Data Scientist, AdNear 26th July, 2014 @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 1/22

2. Structure of this Talk Introduction to a Real Time Bidder(RTB) System design to deliver performance at scale Three speciﬁc Data products that we built Building a low latency self learning system @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 2/22

3. The Data we get our hands on @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 3/22

4. Real time bidding @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 4/22

5. What happens when a mobile user logs in @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 5/22

6. System design Simulation A/B testing framework Reporting Data products & Experimentation Bidder Spark-In-memory processing of logs in δt Update snap- shots in Redis to consume (Multiple) Kafka consumers Access Busi- ness risk target&control groups Parse json logs & dump to Spark Feedback Loop Dumpraw Jsonlogsvia consumers Experiments run live Livefeeds Bidder gets all attributes it needs Online experimentation at Microsoft - Kohavi, Crook, Longbotham(2009) @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 6/22

16. Experiments ProbablityToConvert() Bid() Price() Inputs FeatureEngineering() MetaDataExtraction() Bidder Experiments() OverRideTargetGroup() RollBack() Log() Reporting() Inputs PreTargetingChecks() AB Testing() ReadStateFromRedis() DataFeedAggregator Experiments() LogAndMaintainState() UpdateRedis() NotifyUser() A/B Testing AssignExperiment() AssignTargets() Inputs Splits() Simulation DollarSpend() MinSampleSize() Reporting PacingRate() BidRate() WinRate() CTR() eCPM() eCPC() PercentageLift() Sandbox Tested Set Traﬃc across Experiments Signal UpdateSnapshots Go Live Low Latency feedback Loop Acceptable Risk & TimeFrame @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 7/22

25. Performance at Scale Right Real Estate App Ranking system Right Time In-App engage- ment Staggered Inventory Bidding Right User Mobility & affinity analysis Behavioral Profiling Power users Right Price Dynamic Price Bidding Right Creative Vertical affinity Windowing & Memory Right Geo- location Geo relevance Productizing your Experimentation:Structuring your Data Products @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 8/22

32. What signals can we extract from a weblog(and more..) @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 9/22

33. Problem # 1: Dynamic bidding system Guiding principle Price that we bid at should reflect the quality of inventory probclick = function(Engagementapp,appcategory , SessionContextdepth,length, Engagementcreativeattributes, Engagementvertical , Engagementuserprofile,collaborativeprofile, EngagementHandsetattributes, Timeday,week,seasonality ) @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 10/22

34. Problem # 1 : And, hence the price should reﬂect this Quality price|probclick = Constant1 ∗ (0 ≤ probclick ≤ threshold1)+ Constant2 ∗ (threshold1 ≤ probclick ≤ threshold2)+ Constant3 ∗ (threshold2 ≤ probclick ≤ threshold3) Modelled as a logistic regression with L1 regularization1 with bagging Converges & scales faster for large datasets: Use the start˙params from the last optimization call - Better ﬁt & AUC 1 Bid optimizing and inventory scoring in targeted online advertising - Perlich, Dalessandro, Hook, Stitelman, Raeder, Provost(2012) @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 11/22

35. Representation : Variable importance @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 12/22

36. Problem #2 :Setting the context Chih Lee,Jalali,Dasdan: Real time bid optimization with smooth budget delivery in online advertising(ADKDD, 2013) @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 13/22

37. Problem #2: Comprehensive Mobile App-Ranking system Primary goal: Capture the cream in the apps, at the right time, for a right price Conventional approach : Scrape the # downloads, appcat, in-app-purchases, trends - Lot of noise ! Key observations : Peculiarity in apps wrt time of day, win rate & demand signals Combat Winner’s curse - Uncover a right spread for the price to bid, so we the bid reﬂects the of click, and at a right price The Sweet Spot - tames the market in long turn by shaving oﬀ the bid price & helps in price discovery @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 14/22

38. Problem #2: How we approached this problem Guiding principle: An app that ”delivers” should be in high-demand, and hence ”should” show up with low win rate in the live feeds Stage 1: H0 : CTR depends on Winrate, BidFloor, PriceSpread, Density, Category CTRappidi ,timet = function(Winrateappidi ,timet , Bidﬂoorappidi ,timet , PriceSpreadappidi ,timet , densityappidi ,timet , Categoryappidi ,timet ) δappidi ,time(t+1) = function(Winrateappidi ,timet , Bidﬂoorappidi ,timet CTRappidi ,timet , densityappidi ,timet , Categoryappidi ,timet ) BidPriceappidi ,time(t+1) = δappidi ,timet + Winpriceappidi ,timet @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 15/22

39. Problem #2 : But Mobile Apps have their own nuances The ”outcome” class is yet to come Low win rate could also be because of expectation of high CTR Stage 1 is tightly coupled with campaign budgets Over penalizes a rock star app with too few moments of truth in the last snapshot Other idiosyncracies, broken input pipes, incoherent data @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 16/22

40. Problem #2 cont.. Stage 2: Decay factor of 80% every hour This means that the signal is only 26.2% informative after 6 time periods CTRappidi ,timet = function((0.80)1 ∗ CTRappidi ,timet−1 , (0.80)2 ∗ CTRappidi ,timet−2 , (0.80)3 ∗ CTRappidi ,timet−3 , (0.80)4 ∗ CTRappidi ,timet−4 , (0.80)5 ∗ CTRappidi ,timet−5 ) Stage 3: Different time periods decay differently, for each appidi CTRappidi ,timet = function(CTRappidi ,timet−1 , CTRappidi ,timet−2 , CTRappidi ,timet−3 , CTRappidi ,timet−4 , Winrateappidi ,timet , Winrateappidi ,timet−1 , Winrateappidi ,timet−2 , Winrateappidi ,timet−3 , Winrateappidi ,timet−4 , Winrateappidi ,timet−5 , categoryappidi , densityappidi ,timet ..) Trade-off: dampens the CTR signal, while cushioning for system failures, broken pipes & outliers @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 17/22

41. Problem #2 : But still has another limitation Chicken & Egg Problem ! We need to have suﬃcient mass of each mobile application. Enter Pooled learning algorithms - Hybrid of Fuzzy, Levenshtein distance Distance metric helps map the performing & non-performing apps from mutiple exchanges which means, we have larger ”support” And, we create data points that are better than blind/naive bidding strategy Can we reduce the candidate set? Lot of Bookkeeping to maintain the appids across exchanges What we actually implemented : Hybrid approach of all these models and Iterate multiple times over ! Levenshtein with C bindings & pandas itertuples @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 18/22

42. Problem #3: User-mobility patterns to generate user proﬁles Guiding principle Generate a probabilistic of picture activity patterns & aﬃnity towards activities Data nodes : Users, Categories of places checked in, Category of Apps Represent this as a bipartite graph, then just need to get the top-k, or activate k segments over a certain critical mass Can we do better ? Need to get this for each lat-long, once - Memoization & Book- keeping @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 19/22

43. Problem #3: Representation User 1 User 2 User 3 User 4 Category6 Category7 Category8 Category9 Movies Entertainment Arts Workplace Food Users App category Checked in 0.0568 0.0043 0.0029 0.0091 0.0033 0.0903 0.0903 0.0953 0.456 0.0667 0.0867 @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 20/22

44. Problem #3 :Why Graphs? Tractability of the problem Interesting properties : v∈V deg+, deg−, sink, joining communities Abstraction & reusability - Multiple ways of Similarity of Apps, Users, Places Behavior Dilution & Ghost clicks Better Hypothesis - maturing your data Products @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 21/22

45. What we learnt: Build a self learning, assisted healing system Own the Statistics Cover the base-line : Universal sink Log everything Forward lookup - Abstract the error & Try-catch Proximity to data producer Reuse data Prep cycles, Better still productize it Loose couple each system - fail fast & forward Feature engineering & Meta-data Engine - It’s more than just YOUR data @ektagrover(Twitter)/ekta1007@gmail.com The Fifth Elephant, 2014 22/22

Building a Dynamic Bidding system for a location based Display advertising Platform

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Building a Dynamic Bidding system for a location based Display advertising Platform