Invited talk at WSDM 2014. Abstract here: http://www.wsdm-conference.org/2014/practice-and-experience-talks/ Corresponding paper: http://olivier.chapelle.cc/pub/ngdstone.pdf

1. RESPONSE PREDICTION FOR
DISPLAY ADVERTISING
WSDM ’14
Olivier Chapelle

2. OUTLINE
1.
2.
3.
4.
Criteo & Display advertising
Modeling
Large scale learning
Explore / exploit
2

3. DISPLAY ADVERTISING
Display Ad
3

4. DISPLAY ADVERTISING
• Rapidly growing multi-billion dollar business (30% of internet
advertising revenue in 2013).
• Marketplace between:
– Publishers: sell display opportunities
– Advertisers: pay for showing their ad
• Real Time Bidding:
– Auction amongst advertisers is held at the moment when a user generates a
display opportunity by visiting a publisher‟s web page.
4

5. BRANDING VS PERFORMANCE
PRICING TYPE
• CPM (Cost Per Mille): advertiser pays per thousand impressions
• CPC (Cost Per Click): advertiser pays only when the user clicks
• CPA (Cost Per Action): advertiser pays only when the user performs a
predefined action such as a purchase.
CAMPAIGN TYPE
• Branding
CPM
• Performance based advertising (retargeting)
CPC, CPA
CONVERSIONS
eCPM = CPC * predicted clickthrough rate
eCPM = CPA * predicted conversion rate
5

6. CRITEO
Bill on
CPC
Advertisers
Pay on
CPM
Publishers
Criteo’s success depends highly on how precisely we can predict
Click Through Rates (CTR) and Conversion Rates (CR)
6

7. RECOMMENDATION
Collaborative filtering
Propose related items to a user based on
historical interactions from other users
Over 50% of Criteo driven sales come
from recommended products the user had
never viewed on advertiser websites
7

8. CRITEO NUMBERS
750
10 PB
EMPLOYEES
42
COUNTRIES
18
BILLION
600
$ MILLION
2013 REVENUE
BANNERS
PER DAY
RT BIDS
PER DAY
7500 CORES CLUSTER
2.1
BILLION
250K
AD CALLS
PER SECOND
8
8

9. OUTLINE
1.
2.
3.
4.
Criteo & Display advertising
Modeling
Large scale learning
Explore / exploit
9

10. FEATURES
• Three sources of features: user, ad, page
• In this talk: categorical features on ad and page.
Advertiser network
Publisher network
Advertiser
Publisher
Campaign
Site
Publisher hierarchy
Url
Ad
Advertiser hierarchy
10

11. HASHING TRICK
• Standard representation of categorical features: “one-hot” encoding
For instance, site feature
0
0
1
cnn.com
0
0
0
0
news.yahoo.com
• Dimensionality equal to the number of different values
– can be very large
• Hashing to reduce dimensionality (made popular by John Langford in VW)
• Dimensionality now independent of number of values
11

12. HASHING VS FEATURE SELECTION
• “Small” problem with 35M different values.
• Methods that require a dictionary have a larger model.
12

13. QUADRATIC FEATURES
• Outer product between two features.
• Example: between site and advertiser,
Feature is 1
site=finance.yahoo.com & advertiser=bank of america
Advertiser network
Publisher network
Publisher
Advertiser
Site
Campaign
Url
Ad
 Similar to a polynomial kernel of degree 2
 Large number of values
hashing trick
13

14. ADVANTAGES OF HASHING
• Practical
– Straightforward implement; no need to maintain dictionaries
• Statistical
– Regularization (infrequent values are washed away by frequent ones)
• Most powerful when combined with quadratic features
Quote of John Langford about hashing
At first it‟s scary, then you love it
14

15. LEARNING
• Regularized logistic regression
– Vowpal Wabbit open source package
• Regularization with hierarchical features
Well estimated
backoff smoothing
Small if rare value
• Negative data subsampled for computational reason
15

16. EVALUATION
• Comparison with (Agarwal et al. ‟10)
– Probabilistic model for the same display advertising prediction problem
– Leverages the hierarchical structures on the ad and publisher sides
– Sparse prior for smoothing
• Model trained on three weeks of data, tested on the 3 following days
auROC
auPRC
Log likelihood
+ 3.1%
+ 10.0%
+ 7.1%
D. Agarwal et al., Estimating Rates of Rare Events with Multiple Hierarchies through Scalable Log-linear Models, KDD, 2010
16

17. BAYESIAN LOGISTIC REGRESSION
• Regularized logistic regression = MAP solution
(Gaussian prior, logistic likelihood)
• Posterior is not Gaussian
• Diagonal Laplace approximation:
with:
and:
17

18. MODEL UPDATE
• Needed because ads / campaigns keep changing.
• The posterior distribution of a previously trained model can be used as
the prior for training a new model with a new batch of data
Day 1 Day 2 Day 3
Day 4
Day 5
M0
M1
M2
• Influence of the update frequency (auPRC):
1 day
6 hours
2 hours
+3.7%
+5.1%
+5.8%
18

19. OUTLINE
1.
2.
3.
4.
Criteo & Display advertising
Modeling
Large scale learning
Explore / exploit
19

20. PARALLEL LEARNING
• Large training set
– 2B training samples; 16M parameters
– 400GB (compressed)
• Proposed method: less than one hour with 500 machines
• Optimize:
• SGD is fast on a single machine, but difficult to parallelize.
• Batch (quasi-Newton) methods are straightforward to parallelize
– L-BFGS with distributed gradient computation.
m
20

21. ALLREDUCE
• Aggregate and broadcast across nodes
9
13
37
37
15
1
7
7
37 37
8
5
3
5
3
37
37
4
4
• Very few modification to existing code: just insert several AllReduce op.
• Compatible with Hadoop / MapReduce
– Build a spanning tree on the gateway
– Single MapReduce job
– Leverage speculative execution to alleviate the slow node issue
21

22. ONLINE INITIALIZATION
• Hybrid approach:
– One pass of online learning on each node
– Average the weights from each node to get a warm start for batch
optimization
• Best of both (online / batch) worlds.
Splice site prediction (Sonnenburg et al. ‘10)
S. Sonnenburg and V. Franc, COFFIN: A Computational Framework for Linear SVMs, ICML 2010
Display advertising
22

23. OUTLINE
1.
2.
3.
4.
Criteo & Display advertising
Modeling
Large scale learning
Explore / exploit
23

24. THOMPSON SAMPLING
• Heuristic to address the Explore / Exploit problem, dating back to
Thompson (1933)
• Simple to implement
• Good performance in practice (Graepel et al. „10, Chapelle and Li „11)
• Rarely used, maybe because of lack of theoretical guarantee.
Draw model parameter
according to P( D)
t
Select best action
according to t
T. Graepel et al., Web-scale Bayesian click-through rate prediction for sponsored search advertising in
Microsoft’s Bing search engine, ICML 2010
O. Chapelle and L. Li, An Empirical Evaluation of Thompson Sampling, NIPS 2011
Observe reward
and update model
24

25. E/E SIMULATIONS
MAB with K arms. Best arm has mean reward = 0.5, others have 0.5 − ε.
25

26. EVALUATION
• Semi-simulated environment: real input features, but labels generated.
• Set of eligible ads varies from 1 to 5,910. Total ads = 66,373
• Comparison of E/E algorithms:
– 4 days of data
– Cold start
• Algorithms:
X candidates
– UCB: mean + std. dev.
– -greedy
– Thompson sampling
X selected
Random w
(ground truth)
Generated Y
Learned w
model update
26

27. RESULTS
• CTR regret (in percentage):
Thompson
UCB
e-greedy
Exploit-only
Random
3.72
4.14
4.98
5.00
31.95
• Regret over time:
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28. OPEN QUESTIONS
• Hashing
– Theoretical performance guarantees
• Low rank matrix factorization
– Better predict on unseen pairs (publisher, advertiser)
• Sample selection bias
– System is trained only on selected ads, but all ads are scored.
– Possible solution: inverse propensity scoring
– But we still need to bias the training data toward good ads.
• Explore / exploit
– Evaluation framework
– Regret analysis of Thompson‟s sampling
– E/E with a budget; with multiple slots; with a delayed feedback
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29. CONCLUSION
• Simple yet efficient techniques for click prediction
• Main difficulty in applied machine learning: avoid the bias (because of
academic papers) toward complex systems
– It‟s easy to get lured into building a complex system
– It‟s difficult to keep it simple
See paper for more details
Simple and scalable response prediction for display advertising
O. Chapelle, E. Manavoglu, R. Rosales, 2014
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