David Pardoe

Doran Chakraborty

Peter Stone

The University of Texas at AustinDepartment of Computer Science

TacTex-09: A Champion Bidding Agent for Ad Auctions

Ad Auctions

Ad Auctions

Ad Auctions

Ad Auctions• Which keywords to bid on?

– Who is searching for what?– Who am I advertising against?

• How much to bid?– What are others bidding?– What position will I get?– How many clicks and conversions will I get?

• What ads to display?• How to monitor my advertising

campaign?– What feedback is available?– Use spending limits?

Background

• Much work on mechanism design problem– Varian 2007, Edelman et al. 2007

• Work from an advertiser’s perspective focuses on isolated subproblems (often stylized)– keyword selection: Rusmevichientong and Williamson

2006– multi-auction bidding: Zhou and Naroditskiy 2008– predicting clicks: Richardson et al. 2007

• Trading Agent Competition – Ad Auctions– solve full bidding problem against other researchers– designed by U. Michigan in 2009– follows other successful TAC competitions

Outline

• Introduction• TAC/AA Overview• The TacTex Agent for TAC/AA• Competition Results• Experimental Results• Conclusion

Ad Auction Agents

Advertiser Publisher User

Competition Entrants Environment (Built-in)

Products and Queries

• 9 products• Query format: (manufacturer, type)

– either may be null– 16 total queries

User Behavior• Each user interested in one product• Users cycle through states

– not searching, 4 levels of searching– increasing query specificity, chance of

buying

• Searching users submit one query daily– user sees up to five ads (impressions) – may click an ad (more likely at higher

positions)– may make a purchase (conversion)

Game Format• 8 advertiser agents per game• 60 game days, 10s each• Each day, for each of the 16 queries,

advertisers:– submit a bid (per click), spending limit, and

ad– receive own outcomes:

• impressions, clicks, conversions, costs– see limited information on other advertisers:

• average position when ad was shown

• Agents have limited capacity, product specialties

Outline

• Introduction• TAC/AA Overview• The TacTex Agent for TAC/AA• Competition Results• Experimental Results• Conclusion

TacTex Agent Overview

User Model• Particle filters for each product (users per state)• Filtering based on daily impressions• Update based on known user transition dynamics

Likelihood =Probability of observed impressions

(binomial distribution)

Likelihood =Probability of observed impressions

(binomial distribution)

d – 1impressions

d – 1impressions

ParticleFilter fora particularproduct

Particle

updateduser

population

updateduser

population

- users in one state for one product type

Advertiser Model• Estimate bids of other advertisers• Average of two estimators• First estimator:

– particle filter for each query– joint distribution over all advertiser bids

• Second estimator:– distribution over discrete bids– separate distribution for each query,

advertiser– model probability of bid transitions

• Also estimate spending limits

Two-level OptimizationGoal: determine bid and spending limit for each query to

maximize future profit

Predicted bids and impressions for each query

Capacity, desired conversions

Greedy OptimizerGreedy Optimizer

Optimal bids and resulting profit

Single Day Optimizer:

Multi-Day Optimizer:

Hill climbing searchHill climbing searchSingle Day OptimizersSingle Day Optimizers

Proposed conversion goal for each remaining game day

Expected profit

Outline

• Introduction• TAC/AA Overview• The TacTex Agent for TAC/AA• Competition Results• Experimental Results• Conclusion

Competition Results• IJCAI 2009• 15 teams • Final round: top 8 agents, 80 games

1. TacTex 79,886

2. AstonTac (Aston U) 76,281

3. Schlemazl (Brown U) 75,408

4. QuakTac (U Pennsylvania) 74,462

5. Munsey (U Washington Tacoma)

71,777

6. epflAgent (EPF Lausanne) 71,693

8. UMTac (U Macau) 66,930

7. MetroClick (CUNY & Microsoft)70,632

Competition Results• AstonTAC and Schlemazl:

– slightly higher revenue per conversion– much higher cost per click

• Other agents:– lower cost per click– much lower revenue per conversion

• TacTex struck right balance

Outline

• Introduction• TAC/AA Overview• The TacTex Agent for TAC/AA• Competition Results• Experimental Results• Conclusion

Experiments• 7 other agents from Agent Repository• One (modified) TacTex• 50 games per experiment• Most important (> 3000 drop in score):

– no multi-day optimization– not estimating spending limits

• Moderately important (> 400 drop in score)– add noise to estimated bids of others– add noise to estimated spending limits of others– add noise to own bids (single day optimizer)– no user model

Conclusion and Future Work

• TacTex a complete agent for ad auctions

• Estimates/predicts all values of interest• Optimizes with respect to these values• All agent components contribute to

performance• Future work: improve advertiser

modeling– machine learning to improve bid estimation– predict future bids given estimates