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A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets
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A Brief Introduction of Real-time Bidding Display Advertising and Evaluation Datasets

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  1. A  Brief  Introduc/on  to  Real-­‐/me  Bidding   and  Test  Datasets   Dr.  Jun  Wang   Senior  Lecturer,  Computer  Science,  UCL   Co-­‐founder,  MediaGamma     The  first  event  on  ComputaAonal  AdverAsing  and  Behavior  TargeAng  Meetup      
  2. Premium  Guaranteed   Forward  market   Best  quality  slots  &  highest  prices   Ads  guaranteed  a  future  period   Fixed  Pricing   Phone-­‐based  &  repeAAve   Real-­‐Time  Bidding              Spot  market,  dynamic  pricing              Algorithm-­‐driven  &  automated              Remnant  inventory                        Anonymous  buyers  &  sellers              12.5x  more  volaAle  than  stock  markets   $0   $5   $10   $15   $20   $25   $30   2013   2014   2015   2016   Billions   USA  Display  Ad  Spending  Breakdown   Premium  guaranteed  display   Real-­‐/me-­‐bidding     28%   72%   25%   75%   $23  bn   $25  bn  $21  bn  $18  bn   19%   81%   22%   78%   Programma/c  Summarised   ✖   ✖   ✖   ✔   ✖   ✖   ✔   ✔   ✔   ✔   2
  3. MediaGamma:  Programma/c   Guarantee  exchange  
  4. When  online  adver/sing  goes  wrong   hVp://mashable.com/2008/06/19/contextual-­‐adverAsing/   Web  users  were  unlikely  to  click  a  shoes  ad  that  appeared  along  side  an  arAcle  about   the  rather  gruesome  story  about  severed  feet  washing  up  on  shore    
  5. Real-­‐/me  bidding  
  6. Real-­‐/me  bidding   6  
  7. Real-­‐/me  bidding   “This  is  Lawrence  from  India.  I  was  searching  Recommender   model  in  web  and  found  your  webpage  in  search  engine.   Then,  I  visited  your  webpage  searching  relevant  contents   and  saw  unrelevant  Google  add  in  "Research  Team"  page   (aWached  screenshot).  This  add  might  vary  from  country  to   country.  But  I  feel  it  will  mislead  and  give  wrong  opinion  to   users  who  visit  your  webpage.”                                                                                                                -­‐  Lawrence  from  India  
  8. Life  of  a  display  ad  in  the  RTB   environment:  0.36  seconds   8   Ad Exchange Demand-Side Platform Advertiser Data Management Platform 0.  Ad  Request   1.  Bid  Request   (user,  context)   2.  Bid  Response   (ad,  bid)   3.  Ad  AucAon  4.  Win  NoAce   (paying  price)   5.  Ad   (with  tracking)   6.  User  Feedback   (click,  conversion,  etc.)   User  InformaAon   User  Demography:                  Male,  25,  Student,  etc.   User  SegmentaAons:                Ad  science,  London,  etc.   Webpage   User  
  9. DSP  (Demand  Side  PlaZorm)   25/06/2014  
  10. Bidder  in  DSP
  11. Op/mal  Bidder:  Problem  Defini/on     Bid  Engine  Bid  Request   Bid  Price   11   Input:  bid  request  include     Cookie  informaAon   (anonymous  profile),  website   category  &  page,  user  terminal,   locaAon  etc   Output:  bid  price   Considera/ons:  Historic  data,   CRM  (first  party  data),  DMP  (3rd   party  data  from  Data   Management  Placorm)             What  is  the  op/mal  bidder  given  a   budget  constraint?   e.g.,  Maximise         Subject  to  the  budget  constraint     𝑅=∑( 𝐶𝑙𝑘+ 𝐶𝑜𝑛𝑣∗ 𝑤𝑒𝑖𝑔ℎ𝑡)  
  12. 12   The  General  Process  for  Bidding   Op/misa/on   Red:  hard  constraints   Green:  features   Blue:  models   Note  that  “Frequency  &  recency  rules”  are  also  used  as  features  
  13. Op/mal  bidder:  the  formula/on •  FuncAonal  OpAmisaAon  Problem   –  Dependency  assumpAon:     •  SoluAon:  Calculus  of  variaAons   context+ad  features   CTR  esAmaAon  winning  funcAon   bidding  funcAon   Weinan  Zhang,  Shuai  Yuan,  Jun  Wang,  OpAmal  Real-­‐Time  Bidding  for  Display  AdverAsing,  KDD’14  
  14. Op/mal  bidder:  the  solu/on Weinan  Zhang,  Shuai  Yuan,  Jun  Wang,  OpAmal  Real-­‐Time  Bidding  for  Display  AdverAsing,  KDD’14  
  15. Experiments Offline   Online   Winner  of  the  first  global  Real-­‐Ame  Bidding  algorithm  contest  2013-­‐2014   Weinan  Zhang,  Shuai  Yuan,  Jun  Wang,  OpAmal  Real-­‐Time  Bidding  for  Display  AdverAsing,  KDD’14  
  16. TEST  COLLECTION  
  17. iPinYou  Large-­‐Scale  RTB  Dataset   •  The  first  published  real-­‐world  RTB  dataset   •  From  iPinYou,  the  largest  DSP  in  China   •  9  adverAsers,  10  days  in  2013,  64M  bids,  35GB   •  Web  link:      hVp://data.computaAonal-­‐adverAsing.org  
  18. iPinYou  Large-­‐Scale  RTB  Dataset  
  19. •  Four  types  of  logs:   – Bids   – Impressions   – Clicks   – Conversions   Dataset  Details   hVp://data.computaAonal-­‐adverAsing.org  
  20. hVp://data.computaAonal-­‐adverAsing.org   Dataset  Details  
  21. Test  Evalua/on  Flow   21  
  22. Acknowledgements   Bowei  Chen,   Mathema/cal  Finance   Shuai  Yuan,   Computer  Science   Weinan  Zhang,   Computer  Science  
  23. Thanks  for  your  aWen/on  

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