ビッグデータがもたらすE-Commerceの変革

Dec./03/2014
Yu Hirate
Rakuten Institute of Technology, Rakuten, Inc.
http://rit.rakuten.co.jp/

Introduction• Yu Hirate（平手 勇宇，ひらて ゆう）• Group Manager
Intelligence Domain Group
Rakuten Institute of Technology (R.I.T.)
Rakuten, Inc.
• Research Interest： Data Mining• Bio.
– Received M. Eng. degree from Waseda University in 2005.
– Received Dr. Eng. degree from Waseda University in 2008.
– Research Associate, Media Network Center, Waseda University, Apr.
2004-Mar.2009.
– Joined R.I.T. on April 2009.

2

Contents

１ Big Data in Rakuten
2 Recommender Systems in Rakuten

3 Product Search Assisting System
4 Keyword Trend
5 Utilizing Review Data

6 Japanese Economic Prediction

3

Rakuten, Inc.
 Chairman and CEOHiroshi Mikitani Employees
Non-consolidated 3,762
Consolidated 10,867(as of Dec.31, 2013) FoundedFeb. 7, 1997
 IPO
Apr. 19, 2000
 Capital
109,530 million yen(as of Dec.31, 2013)
Internet Service Company
Core Service : Rakuten Ichiba（E-commerce ）4

1997 → 2014
Our Mission:“Empowering People and Societies through the Internet”

5

Rakuten started from here.

Our EC business model is very unique!

EC sites in US:
Product-centric

Rakuten Ichiba:
Shop-centric7

a golden buddhist altar (金仏壇)
77,700,000 yen

8Did you know… ?

9

Scale Expansion in Rakuten Ichiba

Ichiba GMS （∝ # of transaction）

1.6億# of items# of reviews10

# of Search Requests in Rakuten Ichiba(Nov. 2010 – Nov. 2014)

New Yearw/o login（SP）

3.11

female（SP）
male（SP）
w/o login（PC）
female（PC）
male（PC）

11

Amount of Internet Traffic

Total Amount of internet traffic

Rakuten received 10% of Japanese internet traffic
on Nov. 13, 2013 (Victory Sales).

12

Rakuten Services (Domestic)
E-Commerce

Portal and MediaFinanceTelecommunications

ＴｒａｖｅｌProfessional Sports

13

Rakuten’s Global Expansion

14

R.I.T. is located in 3 locations.

15

Contents

１ Big Data in Rakuten
2 Recommender Systems in Rakuten

3 Product Search Assisting System
4 Keyword Trend
5 Utilizing Review Data

6 Japanese Economic Prediction

16

Recommender Algorithms

17

5ten – Collaborative Filtering

18

0-HO

1. Create word vector for each item
2. Calculate cosine similarities
Term 1

Term 2

Term 3

Term 4101012030102002019

Applied by Various ServicesRakuten Ichiba

Rakuten Books

Rakuten Rental

Rakuten Download
20

TOHO : Recommender Platform
SPDB

Product data

Recommender
Platform

applying tovarious services

User Data
Purchase History
Data
Page View
History Data

Business

Algorithms
Collaborative Filtering
Basket Analysis
Cluster Coefficient
Content Analysis
Retargeting

DB for service21

Identifying the same items【あす楽】 ダイキン 加湿ストリーマ
空気清浄機 TCK70P-W ホワイト

ダイキン（DAIKIN）
加湿空気清浄機
TCK70P-W（ホワイト）
コンパクトモデル
ストリーマー+アクティブプラズマイオン
空気清浄～31畳
加湿:木造11畳/プレハブ18畳前モデル:TCK70N
22

Identifying the same items

商品番号：TJG694-4389
商品名：GT-2000 NEW YORK 2

商品番号：TJG694-7589
商品名：GT-2000 NEW YORK 2

23

Time Awareness Item Recommender
Considering time interval ⇒ purchase reminder• Items in Rice, Wine, Water, Pet Food are purchased
at regular intervals by some users.
• It’s possible to predict when the next purchase will occur.
past
Past

Buy

30 daysBuy

29 daysfutureBuy

32 days

Buy

Remind29 days
24

Time Awareness Item Recommender
Users who purchase the same items

57%

Offer the same item.

Users who purchase different items withthe same price range

79%

Offer different items.

25

Item Recommendation
with Augmented Reality（AR hitoke）

26

Video Recommender27Video RecommenderVideo aggregation by using part / episode Information
131K videos

JungleEmperor
Leo
Part1

JungleEmperorLeo
Part2

80K videos

Monk
Season 3
Episode 1

Monk
Season 3
Episode 2

80K videos
JungleEmperorLeo

23K videos

Monk
Season 328Video Recommender
Probability of videos having same attributes
TV NW > Country > Genre > Actor, ..

Madly in Love

Three Days

Monster

TVNW: SBS
Genre: Romance
Year: 2013

TVNW: SBS
Genre: Action
Year: 2014

TVNW: TvN
Genre: Romance
Year: 201329

Contents

１ Big Data in Rakuten
2 Recommender Systems in Rakuten

3 Product Search Assisting System
4 Keyword Trend
5 Utilizing Review Data

6 Japanese Economic Prediction30

Keyword Suggestion
Suggesting frequently searched keywords

Gathering
SearchLog Data

Calculating
Frequency ofeach keyword

Removing
Noise
Keywords

Applying to
Frontend31

Keyword Suggestion
Gathering
SearchLog Data

Calculating
Frequency ofeach keyword

Removing
Noise
Keywords

Applying to
Frontend

Preventing intentional keyword insertion
from fraud users / merchants

SearchServers

ポールスミス ABAB
ポールスミス ABAB
ポールスミス ABABポールスミス ABAB
32

Item Genre Suggestion
Identifying Rakuten’s genres
which are related to user-input keywords

ワンピース(oneワンピース(onepiece)piece)

33

Item Genre Suggestion
Detecting biases in users’ search behavior

ワンピース(one piece)
Men’s Clothing

Related!

Women’s Clothing
Sports & Outdoors

Related!

Toys, Hobbies & Games
Home Appliances

Related!

Kids, Baby & Maternity
・・・34Item Genre Suggestion
Identifying related genresby referring related genre structure35

Item Genre Suggestion + Keyword Suggestion
Suggesting keywords with related genres
Users are able to specify
both keyword and genre at the same time36

Contents

１ Big Data in Rakuten
2 Recommender Systems in Rakuten

3 Product Search Assisting System
4 Keyword Trend
5 Utilizing Review Data

6 Japanese Economic Prediction

38

Keyword Trend

Search log data is reflected by users’ demand.

Time

2014/11/10

2010/11/01# of search request

Keyword : Christmas Tree

39

Keyword Trend

Discovering peak season from time series data

Dec. 31st

Nov. 3rd

Aug. 24thJan. 1st

Halloween Season starts from 24th Aug.

40

Keyword Trend

Finding unknown correlations
from keyword trend dataKeyword：Father’s day

Keyword: suteteko

41

Keyword Trend
Burst keywords after Great East Japan Earthquake
We can see demands that aren’t reflected in POS data.
Keyword：Bottled Water

Keyword：Batteries

Keyword：Toilet Papers

Keyword：Lantern

2011/03/11

2011/03/11
42

Contents

１ Big Data in Rakuten
2 Recommender Systems in Rakuten

3 Product Search Assisting System
4 Keyword Trend
5 Utilizing Review Data

6 Japanese Economic Prediction

43

GORA Review Analysis

44

Utilizing Review Data (1)• Extracting “attractive” reviews.
• Creating event pages by gathering
attractive reviews.

45Utilizing Review Data (2)46Contents

１ Big Data in Rakuten
2 Recommender Systems in Rakuten

3 Product Search Assisting System
4 Keyword Trend
5 Utilizing Review Data

6 Japanese Economic Prediction47

Predict Japanese Economy by Rakuten’s Big Data

SalesQueryEffect

JP Economy

Composite Index(景気動向指数)

Nikkei Stock Ave.
(日経平均株価)48

Sales → Composite Index: Model• Predict Composite Index by using LASSO• Use monthly sales data in each L4 genre
Sales of Genre A
Sales of Genre B
Sales of Genre CLASSO

Composite Indexat t(t期の景気動向指数):L4 genres, 2521 genres

49

Composite Index(景気動向指数)

Sales → Composite Index: Result

Prediction Error= 0.4%

110

Composite Index (CI)

108
106

104
102
100

ActualPrediction

98

Predict(Training fit)

96

Predict(Test fit)94

2009/12/1

2010/12/1

Month2011/12/1

2012/12/1• Training data : Dec., 2009 – Dec. 2012
• Test data : Jan., 2013 – Apr., 201350

Effective predictors

If following genre sales become larger,
Composite Index will be larger.

jewelry(Cameo)Air
Conditioner

PC

Comedy(Work Station)(Blu-ray)(窓用エアコン)51

Composite Index(景気動向指数)Query → Composite Index: Result

Prediction Error= 0.7%• Training data : Nov., 2010 – Aug. 2013
• Test data : Sep., 2013 – Dec., 201352

Correlated Queries with Composite Index
Positive Impact
ブライトリング ベントレー
Breitling Bentley
アスベル
Asvel
アンダーグラウンド
underground
イヤリング
ear ring
yamadayamadanina mew
nina mewanap
anap
ネストローブnest Robe
ロンハーマンRon Herman
ドナルドダック
Donald duck

0.8481080.844859
0.832566
0.830011
0.829693
0.823915
0.820222
0.818126
0.812800
0.812533

Negative Impact
iphone4 バッテリー
iphone4 battery
マグナムドライMagnum dry
ソーラーライト ガーデンSolar light garden
送料無Free Ship
モデル人形
Model doll
starvations
starvations
シュウィン
Schwinn
springdays
springdays
真木よう子
Yoko maki
keep
keep

-0.729672-0.731111-0.731783-0.731847-0.733298-0.734424-0.735521-0.735996-0.736106-0.737471
53

Nikkei Stock Ave.
(日経平均株価)

Query → Nikkei Stock Average

Mean absolute error is about 1,376 (~8.5%)

Prediction Error= 8.5%• Training data : Nov., 2010 – Aug. 2013
• Test data : Sep., 2013 – Dec., 201354

Thank you!!