Naoki Watanabe (hackmylife) LINE / Development Team A Mainstream ad platforms today employ RTB (Real-time Bidding) where an auction is carried out for one impression in real-time. Faster ad load yields to better performance and the process of auction and ad determination needs to be completed in the matter of hundredth of a second. This is not as simple as returning high bid price - an ad has to be relevant to the audience and to achieve this, audiences' characteristics need to be inferred and have an optimal ad selected via machine learning. Challenges (and fun) with ad platform lies in processing these complex task within a short span of time. The delivery system not only has to be high-speed, but also requires accumulating and analyzing a vast amount of data in real-time. In order to solve this complex problem, LINE has developed various systems on its own such as ad delivery system (SSP and DSP), Ad Ranker (for auction), Data Pipeline (for real-time logging and analysis), DMP (for managing inferred audience attributes and expanding targeting (look-a-like)), and many more. This session outlines reasons LINE has decided to develop most of its ad platform on its own, and the system that LINE has been envisioning.

1. LINE'S VISION OF AN IDEAL
ADVERTISEMENT PLATFORM
Naoki Watanabe

2. LINE Ads Platform
Timeline LINE NEWS LINE Manga LINE BLOG

3. Deliver ads to LINE
Reach 78 million users
Programmatic advertising platform
LINE Ads Platform

4. Programmatic Advertising
● Change budget and delivery target in real time
● Pay per click
● Choose ad by auction

5. Auction Process
Ad Request
Ad (3.5$)
Supply Side Platform Demand Side Platform

6. Only 50ms

7. Real Time Bidding

8. Advertiser
eCPM
Media
UX
Audience
ROI
Satisfy the Three Needs

9. How do you measure?

10. CTR (Click Through Rate)
CTRad =
clicksad
impressionsad
≒ Audience satisfaction
≒ Advertiser purpose
≒ Media benefit

11. Maximize CTR

12. Know the Audience
CTR Prediction
Real Time Analysis
Strategy

13. Know the Audience

14. • Look a like
• Estimate audience attributes
• LINE Tag
Data Management Platform (DMP)

15. ● Male
● Female
● Education
● Automotive
● Game
● Business
Age Gender Interests
Estimate Audience Attributes
● 5-year increment
● 20-24
● 25-29

16. ● JavaScript embedded in web site
● Send conversions and custom events
● Segment audiences who visited a specific website
LINE Tag

17. ● Create audience group
● Find similar audiences
Look a like
Seed audience
Lookalike audience
LINE audience
For more details, see poster session

18. CTR Prediction

19. CTR Prediction
CTRad =
clicksad
impressionsad
pCTRad = P(Click|AD, Request)
= P(Click|AD, Placement, Audience, Time)

20. ● Id
● Advertiser
● Frequency
● Etc..
● Category
● Place
● Time
● Etc..
Audience AD Site
CTR Prediction Features
● Gender
● Age-range
● Interest
● Etc..

21. Feature Dimensions
gender × agerange × time × ad
2 × 9 × 24 × 1,000 = 432,000
For more details, see poster session

22. CTR Prediction on LAP
Feature Millions of feature dimensions
Main Model LR, FM, FFM
Learning Method FTRL online learning
Multi-armed Bandit Contextual Thompson Sampling
Prediction time Real-time (under few msec)

23. • Budget optimization
• CTR(click-through ratio) prediction
• CVR(conversion ratio) prediction
Machine Learning for Ads
• Auto bidding
• Quality scoring
• Dynamic recommendation
• Dynamic creative optimization
• etc..

24. Real Time Analysis

25. • Analysis cluster
• Real-Time logging
Data Pipeline
• Availability

26. Real-Time Logging
● Kafka > Elasticsearch > Kibana
● Monitored by grafana

27. ● Hadoop cluster & Druid for AD
● Analysis of business indicators
● Data source for machine learning
Analytics Cluster

28. ● Apache Mesos + Apache aurora
● Server monitoring
● Auto-scaling
Availability

29. ● Collect as much information as possible
● Real-time machine learning (within 50ms)
● Enhance speed of PDCA (iteration) cycle
Conclusion

30. • Store and analyze huge data
• Stable servers and networks
• Fast and stable data pipeline
Essentials

31. ● Private cloud platform (Verda)
● Fully managed
● Server, LB, DNS, Storage, etc. can be create by UI
Stable Servers and Networks
For more details, see Hall A 13:20

32. ● Kafka cluster
● Message transmission between services
● Queue and job scheduling
Fast and Stable Data Pipeline
For more details, see Hall A 15:00

33. ● Robust Hadoop cluster called Datalake
● Huge database that collects all LINE data
● OASIS and Yanagishima make analysis easy
Store and Analyze Huge Data
For more Datalake details, see Hall A 15:50
For more Oasis details, see Hall B 16:40

34. Support by Professionals

35. Team

36. 100 developers 102 Repositories2 Country
Team

37. ● Online meeting with simultaneous language interpretation
● Chat through LINE translation bot
● Frequent business trips— important to talk face to face
Team Communication

38. Next

39. InventoryGlobal
Challenges

40. Inventory
Machine
Learning
Global
Challenges
InteractiveAR

41. Stay Tuned!

42. THANK YOU