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TacTex-09: A Champion Bidding Agent for Ad Auctions

TacTex-09: A Champion Bidding Agent for Ad Auctions. David Pardoe Doran Chakraborty Peter Stone. The University of Texas at Austin Department of Computer Science. Ad Auctions. Ad Auctions. Ad Auctions. Ad Auctions. Which keywords to bid on? Who is searching for what?

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TacTex-09: A Champion Bidding Agent for Ad Auctions

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  1. TacTex-09: A Champion Bidding Agent for Ad Auctions David Pardoe Doran Chakraborty Peter Stone The University of Texas at Austin Department of Computer Science

  2. Ad Auctions

  3. Ad Auctions

  4. Ad Auctions

  5. 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?

  6. 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

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

  8. Ad Auction Agents Competition Entrants Environment (Built-in) Advertiser Publisher User

  9. Products and Queries • 9 products • Query format: (manufacturer, type) • either may be null • 16 total queries

  10. 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)

  11. 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

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

  13. TacTex Agent Overview

  14. User Model Particle Filter for a particular product Particle d – 1 impressions updated user population Likelihood = Probability of observed impressions (binomial distribution) Particle filters for each product (users per state) Filtering based on daily impressions Update based on known user transition dynamics

  15. - users in one state for one product type

  16. 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

  17. Two-level Optimization Goal: determine bid and spending limit for each query to maximize future profit Single Day Optimizer: Predicted bids and impressions for each query Greedy Optimizer Optimal bids and resulting profit Capacity, desired conversions Multi-Day Optimizer: Proposed conversion goal for each remaining game day Single Day Optimizers Hill climbing search Expected profit

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

  19. Competition Results • IJCAI 2009 • 15 teams • Final round: top 8 agents, 80 games 7. MetroClick (CUNY & Microsoft) 70,632

  20. 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

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

  22. 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

  23. 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

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