1. Ingmar Weber Yahoo! Research Barcelona ingmar@yahoo-inc.com
Please interrupt me at any point!
Online Advertising Open lecture at Warsaw University February 25/26, 2011
Ingmar Weber
Yahoo! Research Barcelona

2. Disclaimers & Acknowledgments
This talk presents the opinions of the author. It does not necessarily reflect the views of Yahoo! Inc. or any other entity.
Algorithms, techniques, features, etc. mentioned here might or might not be in use by Yahoo! or any other company.
Many of the slides in this lecture are based on tables/graphs from the referenced papers. Please see the actual papers for more details.

3. Review from last lecture
Lots of money
Ads essentially pay for the WWW
Mostly sponsored search and display ads
Sp. search: sold using variants of GSP
Disp. ads: sold in GD contracts or on the spot
Many computational challenges
Finding relevant ads, predicting CTRs, new/tail content and queries, detecting fraud, …

4. Plan for today and tomorrow
So far
Mostly introductory, “text book material”
Now
Mostly recent research papers
Crash course in machine learning, information retrieval, economics, …
Hopefully more “think-along” (not sing-along) and not “shut-up-and-listen”

5. But first …
Third party cookies
(many others …)

6. Efficient Online Ad Serving in a Display Advertising Exchange
Keving Lang, Joaquin Delgado, Dongming Jiang, et al.
WSDM’11

7. Not so simple landscape for D A
Advertisers
“Buy shoes at nike.com”
“Visit asics.com today”
“Rolex is great.”
Publishers
A running blog
The legend of Cliff Young
Celebrity gossip
Users
32m, likes running
50f, loves watches
16m, likes sports
Basic problem:
Given a (user, publisher) pair, find a good ad(vertiser)

9. Ad networks and Exchanges
Bring together supply (publishers) and demand (advertisers)
Have bilateral agreements via revenue sharing to increase market fluidity
Exchanges
Do the actual real-time allocation
Implement the bilateral agreements

10. D only known at end. User constraints: no alcohol ads to minors
Which edges can be pruned?
Middle-aged, middle-income New Yorker visits the web site of Cigar Magazine (P1)
D only known at end.
User constraints: no alcohol ads to minors
Supply constraints: conservative network doesn’t want left publishers
Demand constraints: Premium blogs don’t want spammy ads

11. Valid Paths & Objective Function

12. Algorithm A Depth-first search enumeration Worst case running time?
Figure 2 – enumerates all paths – exponential in worst case, linear for trees.
Worst case running time?
Typical running time?

13. Algorithm B US pruning Worst case running time? Sum vs. product?
Optimizations?
Upper bound
Why?
Single source, multi sink Dijkstra E log(N)
Dijkstra dominates
The reversal is at most E
Sort is at most
First prune edges
D pruning

14. Reusable Precomputation
Cannot fully enforce D
Depends on reachable sink …
… which depends on U
What if space limitations?
How would you prioritize?
Pre enforce S and D constraints. Only have to add U later.
Precompute for all s?
Can vary for how many s we precompute things.

15. Experiments – artificial data

16. Experiments – real data

17. Christian Danescu-Niculescu-Mizil, Andrei Broder, et al. WWW’10
Competing for Users’ Attention: On the Interplay between Organic and Sponsored Search Results
Christian Danescu-Niculescu-Mizil, Andrei Broder, et al.
WWW’10
What would you investigate?
What would you suspect?

18. Things to look at General bias for near-identical things
Ads are preferred (as further “North”)
Organic results are preferred
Interplay between ad CTR and result CTR
Better search results, less ad clicks?
Mutually reinforcing?
Dependence on type
Navigational query vs. informational query
Responsive ad vs. incidental ad

19. Data One month of traffic for subset of Y! search servers
Only North ads, served at least 50 times
For each query qi most clicked ad Ai* and most clicked organic result Oi*
63,789 (qi, Oi*, Ai*) triples
Bias?

20. (Non-)Commercial bias?
Look at A* and O* with identical domain
Probably similar quality …
… but (North) ad is higher
What do you think?
In 52% ctrO > ctrA

21. Correlation av. ctrA av. ctrO ctrA ctrO
For given (range of) ctrO bucket all ads.

22. Navigational vs. non-navigational
av. ctrA
av. ctrO
ctrO
ctrA
Navigational: antagonistic effect
Non-navigational: (mild) reinforcement

23. Dependence on similarity
Bag of words for title terms
(“Free Radio”, “Pandora Radio – Listen to Free Internet Radio, Find New Music”) = 2/9

24. Dependence on similarity
av. ctrA
av. ctrA

25. A simple model
Want to model
Also need:

26. A simple model
Explains basic (quadratic) shape of overlap vs. ad click-through-rate

27. Improving Ad Relevance in Sponsored Search
Dustin Hillard, Stefan Schroedl, Eren Manavoglu, et al.
WSDM’10

28. Ad relevance  Ad attractiveness
How related is the ad to the search query
q=“cocacola”, ad=“Buy Coke Online”
Attractiveness
Essentially click-through rate
q=“cocacola”, ad=“Coca Cola Company Job”
q=*, ad=“Lose weight fast and easy”
Hope: decoupling leads to better (cold-start) CTR predictions

29. Basic setup Get relevance from editorial judgments
Perfect, excellent, good, fair, bad
Treat non-bad as relevant
Machine learning approach
Compare query to the ad
Title, description, display URL
Word overlap (uni- and bigram), character overlap (uni- and bigram), cosine similarity, ordered bigram overlap
Query length
Data
7k unique queries (stratified sample)
80k query-ad judged relevant pairs

30. Basic results – text only
Precision = (“said ‘yes’ and was ‘yes’”)/(“said ‘yes’”)
Recall = (“said ‘yes’ and was ‘yes’”)/(“was ‘yes’”)
Accuracy = (“said the right thing”)/(“said something”)
F1-score = 2/(1/P + 1/R) harmonic mean < arithmetic mean
What other features?

31. Incorporating user clicks
Can use historic CTRs
Assumes (ad,query) pair has been seen
Useless for new ads
Also evaluate in blanked-out setting

32. Translation Model In search, translation models are common Here D = ad
Good translation = ad click
Typical model
Maximum likelihood (for historic data)
A query term
An ad term
Any problem with this?

33. Digression on MLE Maximum likelihood estimator
Pick the parameter that‘s most likely to generate the observed data
Example: Draw a single number from a hat with numbers {1, …, n}.
You observe 7.
Maximum likelihood estimator?
Underestimates size (c.f. # of species)
Underestimates unknown/impossible
Unbiased estimator?

34. Remove position bias Train one model as described before
But with smoothing
Train a second model using expected clicks
Ratio of model for actual and expected clicks
Add these as additional features for the learner

35. Filtering low quality ads
Use to remove irrelevant ads
- Don‘t show ads below relevance threshold
Showing fewer ads gave more clicks per search!

38. Second part of Part 2

39. Estimating Advertisability of Tail Queries for Sponsored Search
Sandeep Pandey, Kunal Punera, Marcus Fontoura, et al.
SIGIR’10

40. Two important questions
Query advertisability
When to show ads at all
How many ads to show
Ad relevance and clickability
Which ads to show
Which ads to show where
Focus on first problem.
Predict: will there be an ad click?
Difficult for tail queries!

41. Word-based Model
Query q has words {wi}. Model q‘s click propensity as:
Good/bad?
Variant w/o bias for long queries:
Maximum likelihood attempt to learn these:
s(q) = # instances of q with an ad click
n(q) = # instances of q without an ad click

42. Word-based Model
Then give up …each q only one word

43. Linear regression model
Different model: words contribute linearly
Add regularization to avoid overfitting
of underdetermined problem
Problem?

44. Digression Taken from: http://www.dtreg.com/svm.htm and

45. Topical clustering Latent Dirichlet Allocation
Implicitly uses co-occurrences patterns
Incorporate the topic distributions as features in the regression model

46. Evaluation Why not use the observed c(q) directly?
“Ground truth” is not trustworthy – tail queries
Sort things by predicted c(q)
Should have included optimal ordering!

47. Pavan Kumar GM, Krishna Leela, Mehul Parsana, Sachin Garg WSDM’11
Learning Website Hierarchies for Keyword Enrichment in Contextual Advertising
Pavan Kumar GM, Krishna Leela, Mehul Parsana, Sachin Garg
WSDM’11

48. The problem(s)
Keywords extracted for contextual advertising are not always perfect
Many pages are not indexed – no keywords available. Still have to serve ads
Want a system that for a given URL (indexed or not) outputs good keywords
Key observation: use in-site similarity between pages and content

49. Preliminaries Mapping URLs u to key-value pairs
Represent webpage p as vector of keywords
tf, df, and section where found
Goals:
Use u to introduce new kw and/or update existing weights
For unindexed pages get kw via other pages from same site
Latency constraint!

50. What they do Conceptually: What they do:
Train a decision tree with keys K as attribute labels, V as attribute values and pages P as class labels
Too many classes (sparseness, efficiency)
What they do:
Use clusters of web pages as labels

51. Digression: Large scale clustering
Syntactic clustering of the Web, Broder et al., 1997
How (and why) to detect mirror pages?
“ls man”
Want a summarizing “fingerprint”?
Direct hashing won’t work
What would you do?

52. Shingling w-shingles of a document (say, w=4)
“If you are lonely when you are alone, you are in bad company.” (Sartre)
{(if you are lonely), (you are lonely when), (are lonely when you), (lonely when you are), …}
Resemblance
rw(A,B) = |S(A,w)ÅS(B,w)|/|S(A,w)[S(B,w)|
Works well, but how to compute efficiently?!

53. Obtaining a “sketch” Fix shingle size w, shingle universe U.
Each indvidual shingle is a number (by hashing)
Let W be a set of shingles. Define:
MINs(W) = The set of s smallest elements in W, if |W|¸s
W otherwise
Theorem:
Let : U!U be a permutation of U chosen uniformly at random.
Let M(A) = MINs((S(A))) and M(B) = MINs((S(B))).
The value |MINs(M(A)[M(B))ÅM(A)ÅM(B)|/|MINs(M(A)[M(B))|
is an unbiased estimate of the resemblance of A and B.

54. Proof
Note: Mins(M(A)) has a fixed size (namely s).

55. Back to where we were
They (essentially) use agglomerative single-linkage clustering with a min similarity stopping threshold
Splitting criteria
How would you do it?
Do you know agglomerative clustering?

56. Not the best criterion? IG prefers attributes with many values
They claim: high generalization error
They use: Gain Ratio (GR)

57. Take impressions into account
So far (unweighted) pages
Class probability = number of pages
Weight things by impressions.
More weight for recent visits:

58. Stopping criterion Stop splitting in tree construction when
All children part of the same class
Too few impressions under the node
Statistically not meaningful (Chi-square test)
Now we have a decision tree for URLs (leaves)
What about interior nodes?

59. Obtaining keywords for nodes
Belief propagation – from leaves up …and back down down
Now we have keywords for nodes.
Keywords for matching nodes are
used.

60. Evaluation Two state-of-the-art baselines Relevance evaluation
Both use the content
JIT uses only first 500 bytes, syntactical
“Semantic” uses topical page hierarchies
All used with cosine similarity to find ads
Relevance evaluation
Human judges evaluated ad relevance

61. (Some) Results
nDCG
… slide

62. Digression - nDCG Normalized Discounted Cumulative Gain
CG: total relevance at positions 1 to p
DCG: the higher the better
nDCG: take problem difficulty into account

63. An Expressive Mechanism for Auctions on the Web
Paul Dütting, Monika Henzinger, Ingmar Weber
WWW’11

64. More general utility functions
Usually
ui,j(pj) = vi,j – pj
Sometimes with (hard) budget bi
We want to allow
ui,j(pj) = vi,j – ci,j¢ pj, i.e. (i,j)-dependent slopes
multiple slopes on different intervals
non-linear utilities altogether

65. Why (i,j)-dependent slopes?
Suppose mechanism uses CPC pricing …
… but a bidder has CPM valuation
Mechanism computes
Guarantees

66. Why (i,j)-dependent slopes?
Translating back to impressions …

67. Why different slopes over intervals?
Suppose bidding on a car on ebay
Currently only 1-at-a-time (or dangerous)!
Utility depends on rates of loan

68. Why non-linear utilities?
Suppose the drop is supra-linear
The higher the price the lower the profit …
… and the higher the uncertainty
Maybe log(Ci,j-pj)
“Risk-averse” bidders
Will use piece-wise linear for approximation
Give approximation guarantees

69. Input definition Set of n bidder I, set of k items J.
Items contain a dummy item j0.
Each bidder i has an outside option oi.
Each item j has a reserve price rj.

70. Problem statement Compute an outcome Outcome is feasible if
Outcome is envy free if for all i and (i,j) 2IxJ
Bidder optimal if for all other envy free
and for all bidders i (strong!)

71. Bidder optimality vs. truthfulness
Two bidders i2{1,2} and two items j2{1,2}.
rj = 0 for j2{1,2}, and oi = 0 for i2{1,2}
What‘s a bidder optimal outcome?
What if bidder 1 underreports u1,1(¢)?
Note: “degenerate” input!
Theorem: General position => Truthfulness.
[See paper for definition of “general position”.]

72. Main Results
Definition:

73. Overdemand-preserving directions
Basic idea
Algorithm iteratively increases the prices
Price increases required to solve overdemand
Tricky bits
preserve overdemand (will explain)
show necessity (for bidder optimality)
accounting for unmatching (for running time)

74. Overdemand-preserving directions
The simple case 1 1
10-p1
p1=1 5
9-p2
5-p3
Bidders 2
8-p1
Items 2
7-p2
p2=0 4
3-p3
Explain:
Increase required
Explain:
First choice graph
12-p1
11-p2 3 3
p3=0
2-p3
Explain:
Path augmentation

75. Overdemand-preserving directions
The not-so-simple case 1 1
11-2p1
p1=1
9-p2
5-p3
Bidders 2
8-4p1
Items 2
4-3p2
p2=0
3-p3
Explain:
ci,j matter!
12-3p1
9-7p2 3 3
p3=0
2-p3

76. Finding ov.d-preserv. directions
Key observation (not ours!):
minimize
or equivalently
No longer preserves full first choice graph
But alternating tree
Still allows path augmention

77. The actual mechanism

78. Michael Trusov, Randolph Bucklin, Koen Pauwels
Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site
Michael Trusov, Randolph Bucklin, Koen Pauwels
Journal of Marketing, 2009

79. The growth of a social network
Driving factors
Paid event marketing (101 events in 36 wks)
Media attention (236 in 36 wks)
Word-of-Mouth (WOM)
Can observe
Organized marketing events
Mentions in the media
WOM referrals (through invites)
Number of new sign-ups

80. What could cause what? Media coverage => new sign-ups?
New sign-ups => media coverage?
WOM referrals => new sign-ups? ….

81. Time series modeling Up to 20 days Lots of parameters sign-ups
intercept
linear trend
holidays
day of week
sign-ups
WOM referrals
Media appearances
Promo events
Up to 20 days
Lots of parameters

82. Time series modeling
Overfitting?

83. Granger Causality Correlation  causality
Regions with more storks have more babies
Families with more TVs live longer
Granger causality attempts more
Works for time series
Y and (possible) cause X
First, explain (= linear regression) Y by lagged Y
Explain the rest using lagged X
Significant improvement in fit?

84. What causes what?

85. Response of Sign-Ups to Shock
IRF: impulse
response function
New to me …

86. Digression: Bass diffusion
New “sales” at time t:
Ultimate market potential m is given.

87. Model comparison
197 train (= in-sample)
61 test (= out-of-sample)

88. Monetary Value of WOM CPM about $.40 (per ad)
Impressions visitor/month about 130
Say 2.5 ads per impression
$.13 per month per user, or about $1.50/yr
IRF: 10 WOM = 5 new sign-ups over 3 wk
1 WOM worth approx $.75/yr