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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
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
Billion
s
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
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MediaGamma: Programma/c Guarantee exchange
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
Real-‐/me bidding
Real-‐/me bidding
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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
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
DSP (Demand Side PlaZorm)
25/06/2014
Bidder in DSP
Op/mal Bidder: Problem Defini/on
Bid Engine Bid Request Bid Price
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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)
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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
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
Op/mal bidder: the solu/on
Weinan Zhang, Shuai Yuan, Jun Wang, OpAmal Real-‐Time Bidding for Display AdverAsing, KDD’14
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
TEST COLLECTION
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
iPinYou Large-‐Scale RTB Dataset
• Four types of logs: – Bids – Impressions – Clicks – Conversions
Dataset Details
hVp://data.computaAonal-‐adverAsing.org
hVp://data.computaAonal-‐adverAsing.org
Dataset Details
Test Evalua/on Flow
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Acknowledgements
Bowei Chen, Mathema/cal Finance
Shuai Yuan, Computer Science
Weinan Zhang, Computer Science
Thanks for your aWen/on
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