intelligent electronic commerce service based on understanding of user behaviors

Intelligent Electronic Commerce ServiceBased on Understanding of User Behaviors

April 17th, 2015Yu HirateRakuten Institute of Technology, Rakuten, Inc.http://rit.rakuten.co.jp/

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Contents

１ What is Rakuten?

3 Keyword Trend for Understanding Users’ Demand

2 Product Search Navigation for better UX

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Rakuten, Inc. Chairman and CEO Hiroshi Mikitani Employees Non-consolidated 4,527

Consolidated 11,723(as of Dec.31, 2014)

Founded Feb. 7, 1997 IPO Apr. 19, 2000 Capital 111,601 million yen

(as of Dec.31, 2014) 　　　

Internet Service Company

Core Service : Rakuten Ichiba （ E-commerce ）

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Rakuten Services (Japan Domestic)

E-Commerce Portal and Media

ＴｒａｖｅｌTelecommunications

Finance

Professional Sports

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Rakuten’s Global Expansion

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Our Business Model : B2B2C

Merchants Users

B B C2 2

Product Data Users’ Behavior

Business Intelligence• Demand Forecast

Better UX• Search Assist System• Recommender System

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# of registered products in Rakuten Ichiba

Population of Japan

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

3.11

malefemale

w/o login

male

w/o login

New Year

female

smar

tpho

neP

C

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Contents

１ What is Rakuten?

3 Keyword Trend for Understanding Users’ Demand

2 Product Search Navigation for better UX

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ワンピース (one piece)ワンピース (one piece) Search

Product Category Suggestion

Japanese Manga

# of Search Results : 1.2 million.

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Our category structure is very complicated!!

root

Layer 1 categories : 35

Layer 2 categories : 390

Layer 5 categories : 25,162

Layer 3 categories:3,802 :

Layer 4 categories :19,832

Total # of categories : 49,221

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Product Category Suggestion

ワンピース (one piece)

Identifying product categorieswhich are related to user-input keywords

ワンピース (one piece) Search

(Women’s Clothing)

(Toys, Hobbies & Games)

(Kids, Baby & Maternity)

(One Piece)(Length: Medium)(Length: Long)

(Anime Figures)(Anime, Comics)(One Piece)

(Child Clothes)

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Product Genre Suggestion

Detecting biases in users’ search behavior

Women’s Clothing

ワンピース (one piece)

Men’s Clothing

Sports & Outdoors

Toys, Hobbies & Games

Home Appliances

・・

・

Kids, Baby & Maternity

Related!

Related!

Related!

Women’s Clothing

Toys, Hobbies & Games

Kids, Baby & Maternity

Search

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Product Genre Suggestion

Identifying related genres by referring related genre structure

Women’s Clothing

Anime &Comics One Piece

AnimeFigures

Toys,Hobbies& Games

One Piece

Tops

Kids, Baby& Maternity Kids Kids clothes

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Contents

１ What is Rakuten?

3 Keyword Trend for Understanding Users’ Demand

2 Product Search Navigation for better UX

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Keyword Trend

Search log data is reflected by users’ demand.

Keyword : Halloween

# of

sea

rch

requ

est

Time

2015

/04/

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2010

/11/

01

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Discovering peak season from time series dataJa

n. 1

st

Dec.

31s

t

Aug.

24t

h

Nov.

3rd

Halloween Season starts from 24th Aug.

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Finding Related Keywords

Keyword ： Father’s day

Finding unknown correlations from keyword trend data

Keyword: suteteko

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Disaster Keywords

2011/03/11 2011/03/11

Burst keywords after Great East Japan EarthquakeWe can see demands that aren’t reflected in POS data.

Keyword ： Bottled Water

Keyword ： Batteries

Keyword ： Toilet Papers

Keyword ： Lantern

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Rakuten Ichiba All item data (approx. 156 million items) Review data (approx. 64 million reviews)

Rakuten Travel Facility data (82,458 facilities) Review data (approx. 4.7 million reviews)

GORA 　 (Rakuten’s golf service) Facility data (1,669 facilities) Review data (320,000 reviews)

Rakuten Recipe Recipe Data (approx. 440,000 recipes) Recipe Images (approx. 440,000 images)

Rakuten Auction Evaluation data (approx. 12 million evaluations)

Annotated Data Tsukuba sentiment-tagged corpus (TSUKUBA corpus) Product images dataset with category label  images with character area

http://rit.rakuten.co.jp/opendata.html　Rakuten Data Release

Thank you!!