Weitere ähnliche Inhalte Ähnlich wie 카카오의 광고지능 (Intelligence on Kakao Advertising) (20) Kürzlich hochgeladen (20) 카카오의 광고지능 (Intelligence on Kakao Advertising)5. Audience
(User)
Advertiser
Publisher
AdTech Eco (AdX/RTB/Programmatic Buying)
Traffic Req4Bid
BidImpression
Visit
Ad Selection
- Filtering
- Ranking
- Pricing
Mediation
(Auction)
** Visit-to-Impression is done within 1~200ms
for at least ten-thousand requests per second.
SSP DSP
DMP
Audience Tracking
(MAT/SDK/Pixel)
Transaction log (train)
Audience Info. (target)
Log
Ad
Media
Log
SSP: Supply-side platform
DSP: Demand-side platform
DMP: Data management platform
MAT: Mobile app tracking
AdX: Ad exchange
RTB: Real-time bidding
6. Different dreams among {Audience, Advertiser, Publisher, Platform}
AD
Audience Advertiser
Publisher Platform
Annoying vs Information Marketing channel (ROAS)
Reserve Price Revenue (Adv - Pub)
7. Balance between Revenue and Relevance
Audience Provide relevant and interesting information
Advertiser Gather new or loyal customers through a low cost channel
Publisher Guarantee a stable and predictable revenue source
Platform Maximize overall welfare (& utility) with user satisfaction
8. Effective Cost Per Mille (eCPM)
Expected/estimated revenue per (a thousand) impression(s)
9. eCPM = Bid Amount x Likeness
Revenue:
Amount that an advertiser is willing to pay for desired actions
Relevance:
How much does a user like to do the actions
10. In CPC,
eCPM = BAclick x pCTR
number of clicks / number of impressions
11. Leave (y = 0) Click (y = 1)
Ad | X
X: Traffic properties (ADxUSRxPLx…)
12. Pr(y = 1 | x, ad)
Aggregation of historical data
Learning from historical data
Reactive method vs Predictive method
13. More likely to click
Sum of traffic properties (WTX)
Logistic Regression
(Maximum entropy)
Pr(y = 1|x) =
1
1 + exp(−wTx)
Softmax of binary (1/0) output
Less likely to click
14. Pr(y = 1|x) =
1
1 + exp(−wTx)
Loss = ∥y − ̂y∥2
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15. Find w that minimizes the negative log likelihood (w/ L2 regularization)
Control model complexity
NLL for logistic regression
arg min
w
n
∑
i=1
log(1 + exp(−yiwT
xi)) +
λ
2
∥w∥2
2
16. Stochastic Gradient Descent (SGD)
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Loss/Cost function (w)
(Global) minimum
(Local) minimum
ηt =
α
β + ∑
t
s=1
g2
s
wt+1 = wt − ηtgt
18. Recent advances on response prediction
— FM/FFM/FwFM (**Factorization Machines)
— Deep & Cross Network (DCN)
— Model ensemble
Interaction & latent
Pr(y=1|X) = 1 / (1 + exp(-y*FM(X)))
Nonlinear embedding
New state-of-the-art
19. Pr(y = 1 | x)
= 1 / (1 + exp(-wTx))
** The pictograms are only for explanation, in that it does not imply that Kakao uses such audience information.
20. (Hopefully) Almost activities in Kakao (+ 𝛂)
with cautious treatments
— Law and guidance (i.e., Privacy)
— De-identification and k-anonymity
— Abstraction: Estimation and aggregation
— No merging between internal and external user data
— & technical and economical barriers
21. M F 20 30 40 50 ADF SUBS …
1 0 0 1 0 0 0 1 0 0 0 0 1 0 0
Estimation Aggregation
Classification (Bayesian inference) Gender/Age-band estimation
Simple but syntactic
Clustering / LDA (topic modeling)
Hashing trick
Gradient Boosting Tree
DNN / AE
Effective but slow
Promising as others do
22. GBDT+LR (Facebook)
DCN (Google)
DNN + GBDT (MS)
LDA + LR (Kakao)
2 {0, 1}d
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𝝈
24. Impression Click Conversion
CTR >> CVR Rare event
𝜟t << 𝜟t Conversion dalay
Click
Installation, registration,
purchase, subscription, …
Variety
Segment Personal Granularity
One action Sequence of actions Context (hurdles)
SSP/DSP MAT/SDK/Pixel Data Integrity
26. More topics for better response prediction
— Multi-task learning (for each ad, objective)
— Transfer learning
— Landscape forecasting
— Multi-touch attribution
— Cold-start
— Exploitation vs Exploration
— Thompson sampling
27. eCPM = BA x pCTR
How much to bid?
How to dynamically adjust bids?
Is every audience equally valuable to me?
29. Creative BAclk pCTR eCPM Rank
*PPC
(GSP)
A 1,000 17% 170 1 941
B 1,500 6% 90 4 **500
C 1,200 9% 108 3 1,000
D 800 20% 160 2 540
* PPC = BA * (next eCPM / own eCPM) = next eCPM / own pCTR
** Reserve price = 500
30. Research Offline Test Online Test Production
• Model validity
• Effect simulation
• Validity & revenue
• A/A Test
• A/B/C/… Test
• Random bucket
Problem & ideation Complexity & Stability
31. In theory (model validation)
— Loss (logLoss)
— RIG = 1 - NE (Entropy)
— Calibration = predicted / actual
— AUC
In reality
— Revenue per request
33. 10
More topics beyond this presentation
— Yield optimization (SSP)
— Auction design (GSP/VCG, reserve price/bid floor)
— Fraud/abusing detection
— Targeting/retargeting/LookALike
— Frequency/recency capping
— AdBlock & DNT: Usefulness vs Annoying
— Dynamic/personalized creative generation
— System consideration (e.g., distributed system)
— Knowledge representation beyond audience
35. Bid amount (BA) Response Rate eCPM
≫ Manual setting (by Adv.)
≫ Auto-bidding (BA*)
≫ Impressions (≒ requests)
≫ Viewable impression
≫ Clickthrough rate (CTR)
≫ Conversion rate (CR,
Landscape forecasting (ARIMA, Prophet)
Probability (Logistic Regression)
— Input feature (X)
— Model weight (W)
36. Ranking eCPM = BAimp = BAclk * pCTR = BAconv * pCTR * pCVR
Data/Feature Privacy-free audience data
Embedding Classification (Bayesian) + Topic Modeling (LDA)
Prediction Logistic Regression
Training (Online) FTRL-Proximal
BidAmount Auto-bidding
Targeting Conversion-driven LookALike
38. Some references
- Ad click prediction: a view from the trenches
- Practical lessons from predicting clicks on ads at facebook
- Simple and scalable response prediction for display advertising
- Modeling delayed feedback in display advertising
- Latent dirichlet allocation
- Factorization machines
- Field-aware factorization machines for CTR prediction
- Field-weighted factorization machines for click-through rate prediction in display advertising
- Deep and cross network for ad click predictions
- Model ensemble for click prediction in bing search ads
- Optimal real-time bidding for display advertising
- Feature hashing for large scale multitask learning
- Score lookalike audiences