1. introduction 2. our approach 4. summary · 2017-10-02 · rtb: real time bidding behind the...

（C）Recruit Communications Co., Ltd.

Overview

1. Introduction

2. Our approach

3. Experiments

4. Summary

1


1. Introduction

• What is Display Advertising?– Advertisements appearing on websites

• Main purpose

– Deliver general advertisements and brand messages

2

Example of Display AD

AD


Behind the Scenes

3

DSPSSP

RTB

Advertiser

Impression

AD

AD

AD

Publisher

SSP: Supply-Side PlatformDSP: Demand-Side PlatformRTB: Real Time Bidding



Behind the Scenes

4

DSPSSP

RTB

AdvertiserPublisher

Impression

AD

AD

AD

1.0$

0.9$

0.7$


Behind the Scenes

5

DSPSSP

RTB

AdvertiserPublisher

Impression


AD

AD

AD

1.0$

0.9$

0.7$

Winner!


Behind the Scenes

6

DSPSSP

RTB

AdvertiserAD

AD

AD

Winner!1.0$

0.9$

0.7$

Publisher


AD

Impression


Business Model

• Advertiser pays a publisher when the ad is clicked

• Performance Indicator

– Click Through Rate (CTR)

– Cost Per Impression (CPI)

– Cost Per Click (CPC) ...etc

7


CTR Prediction with Machine-Learning

• Machine-Learning (ML) tech is often used for CTR prediction

• ML has succeeded in this field

8

Click F1 F2 F3

1 M 01 2.13

0 F 07 2.12

0 F 23 4.2

? F 99 1.2

Click or NotPrediction

Matrix expressionUsers

Model

(Click-through-rate)


0

4

8

12

16

0 6 12 18

Budget Pacing

• Budget pacing is also important

• Control of budget pacing helps

advertisers to…

– Reach a wider range of audience

– Avoid a premature campaign stop / overspending

9

Too fast

Budget pacing controlled

Budg

et

spendi

ng

Target budget

Time (hours)


Budget Pacing

• Two lines of strategies– Bid modification– Probabilistic throttling

• We propose a different way based on a bipartite graph and inspired by financial theory

• Our research strengthens existing research rather than replacing it

10


Our Approach: Model and Formulation• Display advertising is

represented by the edge-weighted bipartite matching problem

• Edge weight = CTR(in our research)

11

AdvertisementsUsers

CTR: Click Through Rate


Our Approach: Model and Formulation

• Control the variation rate of CTR

• Find a matching with

– High CTR

– Low variation of CTR0%

1%

2%

3%

4%

5%

12 AM

01 AM

02 AM

03 AM

04 AM

05 AM

06 AM

07 AM

08 AM

09 AM

10 AM

11 AM

AD1 AD2 AD1 & AD212

CTR


Our Approach: Model and Formulation

• Objective Function as QUBO

13

Maximize CTR Low variation Constraint


Experiments with Real World Data

• Setup

– Data: our Display Ad campaign data

– Combination: 14-advertisement campaign and 24 user (cluster)

– CTR and its variation are estimated by historical data• Sample average and variance-covariance matrix

14



• Use quantum annealing processor, D-Wave 2X

• Some techniques for optimization

– Pruned edges with less impact

– Reduce the solution space by clustering users

15

AdvertisementsUsers

Clustered

Removed



• Correlation between edges (CTR correlation)

16


Performance Measure

17

CTR Matrix

Time(Hour)

AD1 x User1

AD2 x User1

AD3 x User1

AD1 x User2

AD2 x User2

Actual CTR

１：Estimate parameters２：Optimize by D-Wave

…

Edge


…

18

CTR Matrix

Time(Hour)

AD1 x User1

AD2 x User1

AD3 x User1

AD1 x User2

AD2 x User2

１：Estimate parameters２：Optimize by D-Wave

Actual CTRPerformance Measure

Edge


Experiments with Real World Data• Hourly performance of each strategy• Greedy method: Choose maximum CTR edge for each user

19

CTR - QA

- Greedy



• Quantum annealing(QA) finds a better solution than the greedy method

– Almost same CTR level

– Low variation of CTR

20

Performance

CTR

Low volatility(≒30% reduced)


4. Summary

• Budget pacing is important for display advertising• Formulate the problem as QUBO• Use D-Wave 2X to solve budget pacing control

optimization problem• Quantum annealing finds a better solution than the

greedy method.

21


Thank you for listening


Memo


References1. Budget pacing for targeted online advertisements at Linkedin

– D. Agarwal, S. Ghosh, K. Wei, and S. You– Proceedings of the 20th ACM SIGKDD, 2014

2. Real time bid optimization with smooth budget delivery in online advertising– K.-C. Lee, A. Jalali, and A. Dasdan– The Seventh International Workshop on Data Mining for Online Advertising, 2013.

3. Online allocation of display ads with smooth delivery– A. Bhalgat, J. Feldman, and V. Mirrokni– Proceedings of the 18th ACM SIGKDD, 2012.

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References• •[Agarwal+ 2014] LASER: A scalable response prediction platform for online advertising,

WSDM 2014 •[Ananthanarayanan+ 2013] Photon: Fault-tolerant and scalable joining of continuous data streams, SIGMOD 2013 •[Chapelle+ 2014] Simple and scalable response prediction for display advertising, TIST 2014 •[Graepel+ 2010] Web-scale bayesian click-through rate prediction for sponsored search advertising, ICML 2010 •[He+ 2014] Practical lessons from predicting clicks on ads at Facebook, ADKDD 2014 •[McMahan+ 2013] Ad click prediction : a view from the trenches, KDD 2013 •[Yan+ 2014] Coupled group lasso for web-scale CTR prediction in display advertising, ICML 2014 •[Yin+ 2014] Estimating ad group performance in sponsored search, WSDM 2014 TokyoWebMining 24

25

HETEROGENEOUS QUANTUM COMPUTING FOR SATELLITE OPTIMIZATION

GID E ON B ASSB OOZ AL L E N HAM ILTON

September 2017

Agenda• Quantum Annealing in the field

• Problem Statement

• Results

• Conclusions

Traveling Salesman

Vehicle Routing

Logistics

Circuit Design

Network Design

Manufacturing

Machine Learning

Artificial Intelligence

Robotics

System Design

Optimization

Combinatorial Chemistry

Drug Discovery

Quantum Annealing has

many real-world

applications

However most

research has been

theoretical


Satellite CoverageQuantum Optimization

Satellite Coverage

Optimization

Summary: Group satellite together in such a way as to maximize coverage.

Data: For any possible grouping of satellites, a coverage percentage

Goal: Assign each of N satellites to k groups, such that total mean coverage is maximized

• Satellites change position and require constant re-optimization

• Brute force solving is out of the question; even trivial subsets of the satellites form too many combinations to check.

• Quantum technology offers a promise to perform combinatorial optimization much faster, while yielding better coverage outcomes.

The weighted k-clique problem

• This problem can be reformulated as a graph problem, called the k-clique problem

• Each potential group of satellites in a sub-constellation can be considered a node on a graph

– Each node is given a weight equal to the coverage provided

– If both sub-constellation use the same satellite, the nodes are unconnected

– The goal is thus to find the k nodes with the highest total weight that are all mutually connected (a "clique")

• This problem can then be expressed as a QUBO, and sent to the quantum computer

Designing the QUBO

Constraints:1. Choose only

nodes that are connected

2. Maximize the sum of coverages for each group chosen

3. Choose a number of qubits equal to the number of available satellites

Each (logical) qubit represents a potential grouping of satellitesConnections represent a grouping that is non-overlapping (does not use the same satellite in multiple groups)

W is the qubit

maximum weight

Quantum Hardwareis rapidly maturing

• This satellite optimization problem is a prime candidate for a quantum approach when used in concert with classical computing resources.

• The application to satellites could be the first major quantum success when applied to a real-world full-scale problem.

• However, with current numbers, we would still need 10^4-10^5 qubits to fully embed this problem

• Thus, we created a heterogeneous approach that combines classical processing and quantum annealing

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Heterogeneous techniques:

TWO APPROACHES

Genetic Algorithm

All sub-constellations

SC SCSCSC

Quantum Computer

Post-processing

Remove low coverage/Pick random

All sub-constellations

Quantum Computer

Post-processing

GA + ANNEAL PRUNE/RANDOM + ANNEAL

Heterogeneous Computing

Models

Method Pros Cons

Classical Heuristics Can provide fairly good results. Can be run onclassical machine.

Cannot be run on current QA devices, no quantum speed-up, scaling uncertain

GA pre-processing Searches full decision space, produces solid results

Middle of the road performance and speed, many parameters to tune

Prune and Anneal Very good results in good time, most similar to existing technique

Does not explore full solution space, requires domain knowledge

Results Comparison:

Quantum Simulator

Purely Classical Genetic

Algorithm

Simulated Quantum Prune

and Anneal

• An 80% coverage(red) is the minimum acceptable average.

• The eight colored bars represent individual sets, black bar (and dotted

line) is overall average

• Quantum approach is faster and finds a significantly better results

Results Comparison:

D-Wave

• Results are nearly constant with processing time

• Results are highly dependent on pre-processing method (color)

• 80% is minimal acceptable

• 90% is likely near the true maximum.

Results Comparison:

D-Wave

• D-Wave time makes up

most of the time, GA adds

a little more

• Including D-Wave’s

“Virtual Full Yield” does

not significantly change

performance while

improving portability

SummaryMethod Uses

Domain-Knowledge

Time Needed

Performance

Prune + Anneal

ü Very Little 90%

GA + Anneal

X Some 80-85%

Random + Anneal

X Very Little 75-80%

• The D-Wave functions best as a co-processor

• Performance is highly dependent on problem formulation, classical

processing step

• Quantum portion does appear to provide significant improvement.

Conclusions

• As problems and datasets grow, modern computing systems have had to scale with them. Quantum computing offers a totally new and potentially disruptive computing paradigm.

• For problems like this satellite optimization problem, heterogeneous quantum techniques will be required to solve the problem at larger scales.

• Preliminary results on this problem using heterogeneous classical/quantum solutions are very promising.

• Exploratory studies in this area have the potential to break new ground as one of the first applications of quantum computing to a real-world problem

GIDEON BASSBOOZ ALLEN HAMILTON

Thank You

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1. introduction 2. our approach 4. summary · 2017-10-02 · rtb: real time bidding behind the...

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