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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