2019/3/28に開催したRakuten Tech Meetup #1 事業に響くデータとAIの発表資料です。

1. 楽天市場データ+機械学習による
予測事例の紹介
Takashi Umeda (梅田卓志)
Rakuten Institute of Technology
Rakuten Inc.
• LinkedIn: https://jp.linkedin.com/in/takashi-umeda-a291808
• Eight: https://8card.net/p/39626131477
• Twitter: @umekoumeda

2. 2
梅田 卓志 (Takashi Umeda)
2016-2018: Finance
• Investment
• Credit Scoring
• Support of marketing activities
2014-2015: Digital Contents
• Recommender / Personalization
• Video popularity prediction
2009-2013: EC
• Demand forecasting
• Prediction for users’ purchase timing

3. 3
What is
“Rakuten Institute of Technology”?

4. 4
Location & members
Strategic R&D organization in Rakuten Group
+150 researchers all over the world
Japan USSingapore
France
India

5. 5
Research Area of RIT
3 research groups for adapting to internet growth
RealityIntelligencePower
• HCI
• MR / AR
• Image Processing
• Robotics
• HPC
• Distributed Computing
• Programing Language
• Machine Learning
• Deep Learning
• NLP
• Data Mining

6. 6
Research Area of RIT
3 research groups for adapting to internet growth
RealityIntelligencePower
• HCI
• MR / AR
• Image Processing
• Robotics
• HPC
• Distributed Computing
• Programing Language
• Machine Learning
• Deep Learning
• NLP
• Data Mining

7. 7
Mission of Intelligence Domain Group
Assist Rakuten businesses to boost with ML and DM
Optimizing A/B testing
Item Classification
User Segmentation
Data + AI
Coupon Distribution
Recommender System Economy Prediction / Demand Prediction
Review Analysis
Anomaly Detection / Fraud Detection
Product Data Analysis
Business

8. 8
Case-1
Prospective User Extraction

9. 9
Mission of Intelligence Domain Group
Assist Rakuten businesses to boost with ML and DM
Optimizing A/B testing
Item Classification
User Segmentation
Data + AI
Coupon Distribution
Recommender System Economy Prediction / Demand Prediction
Review Analysis
Anomaly Detection / Fraud Detection
Product Data Analysis
Business

10. 10
Goal
Ichiba Active Users
Prospective
users
Extract Financial
Service
Find out particular financial service users from ICHIBA users

11. 11
Approach
Ichiba
Active Users
Overlap
7,413
Positive Samples
7,417
Negative Samples
About 50% of the financial service users were Ichiba Active Users
Financial Service
Users

12. 12
Model
Input
Interest rate
Predict who will use particular financial service
• Purchase trend
• Purchase of each L2 genre
• GMS trend, Frequency
• Basic Demogra.
• Gender, Area, Age
Model
Output
Interest rate
Will be user
Won’t be user

13. 13
What kinds of Rakuten ICHIBA users
are most likely to apply for the target service?

14. 14
Significant Factors
0 0.05 0.1 0.15 0.2 0.25
genre_41_100890_ / 花・ガーデン・DIY / DIY・工具
genre_72_111078_ / キッズ・ベビー・マタニティ / キッズ
genre_50_110983_ / 靴 / メンズ靴
Age-05-[35-40]
genre_93_101077_ / スポーツ・アウトドア / ゴルフ
Area-01-Kanto
Area-00-Others
genre_113_101126_ / 車用品・バイク用品 / カー用品
Age-08-[50-*]
Age-03-[25-30]
Age-00-none
gms
Gender-00-none
basket_max_price
frequency
basket_average_price
average_unit_price
Age-02-[20-25]
Gender-02-female
Gender-01-male
Top 20 factors selected from 141 factors
Ichiba Genre /Cars & Motorcycles/Car Accessories
Ichiba Genre /Sports & Outdoors/Golf
Ichiba Genre /Shoes/Men’s Shoes
Ichiba Genre /Kids & Baby/Kids
Ichiba Genre /Gardening & Tools / DIY Tools
average_unit_price
basket_average_price
frequency
basket_max_price
gms
Loyalty
Life Stage

15. 15
Evaluation #1
Prospective Users Control Group
• Randomly Selected
• About 300,000 users
• Score >= 0.8
• About 300,000 users
Send ichiba mail magazine to two groups
Ichiba Mail Magazine

16. 16
Evaluation #2
Mail Deliver
Open Mail
Click Contents
(Visit Service
Page)
Click Rate went up by +49.23%
compared with control group
+3.52% +49.23%

17. 17
Application in other services
Prospective
users
Extract
Target
service
Same scheme can be applied to various kinds of services
Ichiba Active Users

18. 18
Platform-nization
Algorithm: RIT
Platform: Data Science Dep.
Rakuten Businesses National Clients
Customer
DNA
• Input: Users who uses the particular service
• Output: Users who will use that service

19. 19
Platform-nization
Algorithm / Model : RIT
Platform / Interface : DSD
Rakuten Businesses National Clients
Customer
DNA
• Creating a platform for the solution together with GDSD.
• Integrating Customer DNA for User Features

20. 20
Current statistics (1/2)
National
Clients

21. 21
Current statistics (2/2)
https://corp.rakuten.co.jp/news/update/2018/0522_01.html accessed on 2019-03-25

22. 22
Case-2
Prediction of JP Economy

23. 23
Mission of Intelligence Domain Group
Assist Rakuten businesses to boost with ML and DM
Optimizing A/B testing
Item Classification
User Segmentation
Data + AI
Coupon Distribution
Recommender System Economy Prediction / Demand Prediction
Review Analysis
Anomaly Detection / Fraud Detection
Product Data Analysis
Business

24. 24
Question
70
75
80
85
90
95
100
105
110
115
120
2008-1
2008-4
2008-7
2008-10
2009-1
2009-4
2009-7
2009-10
2010-1
2010-4
2010-7
2010-10
2011-1
2011-4
2011-7
2011-10
2012-1
2012-4
2012-7
2012-10
2013-1
2013-4
2013-7
2013-10
2014-1
CompositeIndex
Month
Composite Index (景気動向指数)
?Abenomics
LEHMAN
Shock
Japanese economy will be better ?

25. 25
Objective
Predict Japanese economy by Rakuten’s data
Composite Index
(景気動向指数)
Effect
JP
Economy

26. 26
Objective
Predict Japanese economy by Rakuten’s data
Composite Index
(景気動向指数)
Effect
JP
Economy
Sales of each category

27. 27
Model
• Predict Composite Index by using LASSO
• Use monthly sales data in each L4 genre
Effect
Sales at t (genre a)
Composite Index
at t
(t 期の景気動向指数)
LASSO
Sales at t (genre b)
Sales at t (genre c)
:
L4 genres, 2521
genres

28. 28
Prediction Results
Mean absolute error is about 0.4
• Training data : Dec., 2009 – Dec. 2012
• Test data : Jan., 2013 – Apr., 2013
94
96
98
100
102
104
106
108
110
CompositeIndex(CI)
Month
Prediction of Composite Index
Actual Predict(Training fit)
Predict(Test fit)

29. 29
What kinds of categories are affecting
Japanese Economy?

30. 30
Effective Predictors
jewelry
(Cameo)
Air
Conditione
r
(窓用エアコン)
PC
(Workstation)
Comedy
(Blu-ray)
If following genre sales become larger, composite Index will
be larger.

31. 31
Summary

32. 32
Mission of Intelligence Domain Group
Assist Rakuten businesses to boost with ML and DM
Optimizing A/B testing
Item Classification
User Segmentation
Data + AI
Coupon Distribution
Recommender System Economy Prediction / Demand Prediction
Review Analysis
Anomaly Detection / Fraud Detection
Product Data Analysis
Business

33. 33
See you later in the round table discussion!
Data
• Contribution for existing
business
• New Business Dev.
AI