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

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

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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


Experiments

Offline Online

Winner of the first global Real-‐Ame Bidding algorithm contest 2013-‐2014


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

Data & Analytics

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