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TAC Ad Exchange Game The Case for Mediating Industry and Research Interplay
Mariano Schain
Disclaimer: The opinions expressed herein are my own and not necessarily those of my employer(s).
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Agenda
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Agenda
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SimulationGamesandTAC
Travel Agents Manufacturers Merchants Brokers Ad Networks
Classic2002
SCM2003
Ad Auctions2009
PowerTAC2011
Ad Exchange2014
Abstraction (Simulation) : Controlled, Repeatable, Transparent
What are the essential elementsof a strategy, a setting? How Good is an algorithm?
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ForExample...TACAA
User
Publisher/Auctioneer
Query
Advertiser
Auction
Impression Page View Click Conversion
Bid, Ad, Limit
Query Reports
Sales Reports
3
3 2a
2b
2c
1
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ForExample...TACAA1. Applicability: Robustness [HSM’13]
User
Publisher/Auctioneer
Query
Advertiser
Auction
Impression Page View Click Conversion
Bid, Ad, Limit
Query Reports
Sales Reports
3
3 2a
2b
2c
1
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−3 −2 −1 0 1 2
−3−2
−10
12
comp 1
com
p 2
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Tau 11TacTexMertacorHermesCrocodileSchlemazlPoleCATAA−HEU
0
10000
20000
30000
40000
50000
60000
70000
80000
2000 4000 6000 8000 10000 12000 14000 16000 18000
TacTex 10
TacTex(2) 10
Tau 11
Schlemazl 10
Tau 10
Crocodile 11
Mertacor 11
EpflAgent 10
3. Diminishing importance of modeling accuracy
Offline
TM
KNN
Game Logs
Lazy Classifier
WEKA
Daily reports (# Bidders)
Total Imps
PF
2. Identify agents: behavioral attributes
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Agenda
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Return, Efficiency Yield, Cost
TargetingLiquidity AccessDemand
Adaptability
Advertiser Publisher
Advertiser Publisher
Advertiser
PublisherAdvertiser
Advertiser
OPT
Price
Publisher
Publisher
Publisher
Advertiser
Advertiser
Advertiser
ADXADN
The evolution of the display-ad advertising industry
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Audience Attributes:Age, Gender, Income..
TheAdX Setting
Contract:ReachTarget Audience
Win Advertising Campaign Contracts:
Budget, QualityExecute CampaignsBy Bidding at AdX
Matched Targeted AudienceAnd Requested Reach:
Quality Rating
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Attributes:Age, Gender, Income
Orientation
Second Price Auction
Campaign allocation:• Quality-based• Auctioned
Target of competing Ad Network: Maximize total net earningsTradeoff: Short term vs. Long term
AdXGameSetting:TheAdNetworkProblem
Interesting Mechanisms: Value of user classification service (cookie matching), Publisher’s reserve price, Quality rating, Campaign allocation, RTB @ AdX, and more..
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TheAdNetworkDecisions
AdNWebsite
Advertiser
Advertiser
Advertiser
Impression Opportunity
Bid
Allocation
Allocation
Allocation
AdX
Real Time
Periodically
ReachTarget
Contract Opportunity
Bid (budget)
AdvertiserAdN UCSBid
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TheAdXGame
12
Based on SICS/TAC-Ad Auctions:Similar “Look and Feel”: Server / Competing Agents, Simulated user
population, 60 Days, Daily Reports and Decisions.
Server
Agent
Agent
Agent
WebBrowser
RegistrationGame ViewerLogs Access
Game
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13
AdXGameFlow
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Mechanisms
User Population Simulation
Real Web sites Statistics
Publisher
Reserve Price Optimization
AdX Real-Time Second-Price Auction
Bid Bundle Proxy
User Classification Service
Value Discovery through Auction
AdNet Quality Rating, Contract Auction
Max/Min BudgetQuality Squash
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15
Users
AttributesAge Gender Income
18-24 25-34 35-44 45-54 55-6465+
Segments Young Old
Male Female
M F
0-30K 30--60K
60--100K 100K+
Low High
Contract Target Audience:Segments of level 1, 2, or 3:
Bid Bundle Entries
Y OL YFH
Web Site, Segment Bid, Contract, Weight
Campaign Execution Quality
AdNet Revenue, Quality: Contract Reach (Target Audience) Vs Actual
Real Web SiteOrientation
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16
ThereIs(Much)More..
Access Device:Desktop, Mobile
Repeated Visits
Unique Impressions
Relative Popularity
Reserve Price Optimization
Ad Types: Video, Text
AdXBid Bundle
Daily Limits:Impressions,
BudgetContract Allocation:Random,Bid Range Restrictions
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TheAdNet Problem
1
2
i
m
contracts
1
ImpressionOpportunities
2
j
n
𝑥"#, 𝑎"#𝑝#
𝑟"
Related Models:• Publishers Problem [BFMM’11] • AdWords Problem [DH’09] • Online Stochastic Packing [FHKMS’10]
Related Decisions:• Reserve price, contract allocation• Allocation to budget-constrained bidders
Given cost and utility functions, and relevance of each impressions opportunity – Decide allocation
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AdX Game– InPractice
Yearly Workshops• Undergrad teams• Agents Repository - Top performers take part in TAC-AdX• Concluding Competition• Project reports
TAC-AdX• 2014 – AAMAS Report by Champion• 2015 – Technical University of Crete,
Brown University, University of Edinburgh, and Tel-Aviv University
Insights• Agent Strategies• ’AdNet Problem’ – still open?!
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AdX Game– InPractice..
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AdX Game– InPractice..
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AdX Game– InPractice..
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AdX Insights– AgentsStrategies
AdN
Impression Opportunity
Bid
Allocation
Allocation
Allocation
AdX
Contract Opportunity
Bid (budget)
UCS
BidUCS: • Winner’s Curse.
• Fierce competition at ‘Must Win’ first days.
• Zero if no contracts.
• Learn (from logs) winning price for required level.
• Lower on last days.
• Adjusted to contract fulfillment levels
• Adjusted to targeted segment size (‘unknown’ risk)
AdX – Impression prices: • Estimate competition level
• Learn price as function of popularity
and time-lapse [TWC’15]
• Adjust to contract fulfilment levels.
• Metric: Bid/price variance
Quality Rating is Crucial:
Phases: Opening/Mid/End-Game
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AdX Insights- ContractAllocation
Random allocation: Allow recovery
Rating: Updated upon contract completion.Used to squash bids and determines bids range.
Related Agents’ Strategies:
• Learn winning prices (based on campaign attributes)
• Regularized bid – by outstanding won campaigns
• Maintain ‘desperate’ levels – Update upon non-random result [TWC’15]
• Bid low if not-profitable (rely on random).
• Small reach campaigns are easy opportunities to improve rating.
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AdX Insights- ReservePriceOptimization
baseline
range
Original: Adaptive
Insight [ANL’15] - Bidding high increases reserve prices
Research:[CGM’13] Regret Minimization, Censored Info[MM’16] Contextual (User Attributes), Full Info
Train/Learn:
New in TAC-AdX 2016: One of Original/None/[MM’16]Convergence !?Publisher’s Revenue !?
(𝒙, 𝑏+,𝑏,) : using 𝑟 𝑥 = 𝑊𝑥 𝑊 = argmin𝑅𝑒𝑣𝑒𝑛𝑢𝑒(𝑟)
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Agenda
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26
ANewEra
TradingAgent
Competition
Classic2002
SCM2003
Ad Auctions2009
PowerTAC2011
Ad Exchange2014
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ANewEra– (User)Agents
Merchants
Energy Brokers
Ad Networks
Avatar/Bot/’Thing’
New Challenges• Scale of Strategic Entities • Distributed Presence
New/Relevant Applications• Shopping• Negotiation• Choice/Elections• Navigation• Health Care• Trust/Security• Financials/Banking• Personal Assistant
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ANewEra- Trading
Valuable, Tradeable Assets(What we.. Sense/Are/Know)• Sensors• Identity, Habits• Information, Knowledge
Multiple Objectives !New Challenges• Heterogeneous Marketplace
Tradeable Training Data!
New/Relevant Applications• Shopping• Negotiation• Choice/Elections• Navigation• Health Care• Trust/Security• Financials/Banking• Personal Assistant
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ANewEra- Competition
Infinitesimal value of a data point vs value of aggregated data • Pooling as alternative to mediators/platforms
Collaboration models• IoT/Bots
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SummaryTAC As a Test-bed/Platform• Insights from AdAuctions• AdX Game
AdX Insights• AdNet Problem still open• Reserve Price Optimization• Winning strategies
Next Gen TAC• Enter the Strategic User-agent • Data Explicitly Traded• Coopetition• Myriad relevant applications
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• A Model-Free Approach for a TAC-AA Trading Agent. [Schain, Hertz, Mansour 2013]• An Empirical Study of Trading Agent Robustness [Hertz, Schain, Mansour 2013]• Ad Exchange - Proposal for a New Trading Agent Competition Game [Schain, Mansour 2013]. • Internet Advertising and the Generalized Second Price Auction [Edelman, Ostrovsky, Schwarz, 2007]• Ad Exchange: Research Issues [Muthukrishnan, 2009]• The Design of Advertising Exchanges [McAfee, 2011]• Yield Optimization of Display Advertising with Ad Exchange [Balseito, Feldman, Mirrokni, Muthukrishnan, 2011]• Improving the Effectiveness of Time-Based Display Advertising [Goldstein, McAfee, Suri 2012]• To match or not to match: Economics of cookie matching in online advertising [Ghosh, Mahdian, McAfee,
Vassilvitskii, 2012]• TAC AdX'14: Autonomous Agents for Realtime Ad Exchange [Tao, Wu, Chen 2015]• Learning Algorithms for Second-Price Auctions with Reserve [Munos Medina, Mohri 2016]• Regret minimization for reserve prices in second-price auctions [Cesa-Bianchi, Gentile, Mansour 2013]• Online Stochastic Packing Applied to Display Ad Allocation [Feldman, Henzinger, Korula, Mirrokni, Stein 2010]• The adwords problem: online keyword matching with budgeted bidders under random permutations [Devanur,
Hayes 2009]
References
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