1. 楽天における機械学習アルゴリズムの活用
Yu Hirate, Dr. Eng.
Rakuten Institute of Technology Tokyo,
Rakuten, Inc.

2. 2
平手勇宇
 Principal Scientist, Rakuten Institute of Technology
Manager, Intelligence domain research group
 Bio.
• 2005-2008 CS div. graduate school of Science and Engineering,
Waseda University.
• 2006-2009 Research Associate, Media Network Center, Waseda
University.
• 2009- current Rakuten Institute of Technology．
Working on projects for extracting knowledge from large scale of data
by utilizing data mining, machine learning technologies.

3. 3
Masaya Mori
Global head
• Established in 2006.
• Launched R.I.T. NY in 2010.
• Launched R.I.T. Paris in 2014.
• Launched R.I.T. Singapore / Boston in 2015.
Strategic R&D organization for Rakuten Group

4. 4
+100 researchers in 5 locations

5. 5
3 research groups for adapting with Internet growth
RealityIntelligencePower
• HCI
• AR / VR
• Image Processing
• Distributed Computing
• HPC
• IoT
• Machine Learning
• Deep Learning
• NLP
• Data Mining

6. 6
Optimizing A/B testing
Item Classification
User Segmentation
AI
Coupon Distribution
Recommender System Economy Prediction / Demand Prediction
Review Analysis
Anomaly Detection / Fraud Detection
Image Recognition

8. 8
Huge!
Unstructured!
241 million items
Num.ofItems(million)
date
https://item.rakuten.co.jp/kawahara/345812/

9. 9
Why are we working on this problem? (Key Benefits)
‣ To organize our catalog in accordance with customer
expectations
‣ To precisely search our catalog for products and its variants
‣ To measure and enforce merchant KPI's.
What are we doing? (Key Tasks)
‣ Product Genre Classification
‣ Attribute Extraction from Product Information
‣ Merchant and Item Review Analysis
How are we doing? (Key Technologies)
‣ Large-Scale Gradient Boosted Decision Trees
‣ Deep Learning (RNN's, CNN's, others)
‣ Computing Massive Number of NLP Features
Product Catalog
Businesses

10. 10
Each product can be assigned a category and attributes. For instance:
+Category Grocery & food
Subcategory Wine
Each (sub)category has a number of relevant attributes with a list of valid values
Challenge: this structured information is not always present or correct
Goal: automatically predict category and attributes from text and/or images
https://item.rakuten.co.jp/kawahara/345812/

11. 11
Classifier based on
Deep Learning Algorithm (CNN)
Prec@1 92%
Prec@10 99%
Classifier based on
Deep Learning Algorithm(CNN)
Prec@1 57%
Prec@3 75%
Extracting Words
* Tested to Ichiba L3 category (1.5K categories)
* Tested for PriceMinister Image Data
Text Data
• Item Title
• Item Description
Image Data

12. 12
Hobby and Entertainment
> Books and Magazine
> Business Electronics
> Audio
> Earphone / Headphone
Electronics
> Smartphone
> AC Adaptor / Battery

13. 13

14. 14

15. 15
Detect prospective applicants from Ichiba purchasers
by using their purchase trends and demographics
Ichiba Active
Users Prospective
Applicants
Extract a finance service

16. 16
Ichiba
Active UsersOverlap
7,413
Positive Samples
7,417
(Negative) Samples
About 50% of contractors of the Fintech service were Ichiba Active Users.

17. 17
抽出されたユーザ行動モデル：重要なファクタ
0 0.1 0.2 0.3
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
市場ジャンル/車・バイク/車用品・バイク用品
市場ジャンル /スポーツ・アウトドア/ゴルフ用品
市場ジャンル/靴/メンズ靴
市場ジャンル/キッズ・ベビー・マタニティ/キッズ
市場ジャンル/ガーデン・DIY・工具/ DIY・工具
購買商品の平均単価
一回あたりの購買金額の平均値
購買頻度
一回あたりの購買金額の最大値
購買金額総計

18. 18
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

19. 19
Mail Deliver
Open Mail
Click Contents
(Visit Service
Page)
Click Rate went up by +49.23%
compared with control group
+3.52% +49.23%

20. 20

21. 21
我们真的很有诚意了。
你说我一个老总都亲
自跑了好几趟了。
Machine
translation
is a Rakuten group company which provides video streaming service.
Volunteers are editing subtitles and translated subtitles.
https://www.viki.com/?locale=ja

22. 22
 Translate from Chinese to English sentences
 Extracted 10,000 Chinese-English sentence-pairs to
evaluate commercial APIs and IBot, e.g.,
 我一个老总都亲自跑了好几趟了
 I’m a director and yet I’ve made so many trips
 Extracted another 2.1 million sentence-pairs to train
IBot’s model

23. 23
 Applying Attentional Recurrent Neural Networks
(RNN)
 Neural Machine Translation by Jointly Learning to
Align and Translate [Bahdanau, Cho & Bengio, ICLR 2015]
 658 citations (Google scholar)
 Train RNN with 2.1 million c
Chinese-English sentence
pairs

24. 24
 Evaluated on 10,000 Chinese-English sentence pairs
System BLEU (%) METEOR (%)
Google API 12 20
Microsoft API 12 20
IBM Watson API 3 12
RIT (Aug 24) 10 15
RIT (Sep 7) 14 19
RIT (Sep 21) 22 24
RIT (Nov 28) 36 30