Various Data

Visualize

Useful insight

Ichiba GMS （∝ # of transaction ）

# of items # of reviews

160 M

Growing Data in Rakuten

…will be happening in Brazil as well

Source: eMarketer Jan 2014 “Retail Ecommerce Sales in Brazil to See Double-Digit Growth This Year”

Growing services with Growing Data

Vol.01　 Oct/14/2014Yoichi Yoshimoto | MarioRakuten Institute of Technology, Rakuten Inc.http://rit.rakuten.co.jp/

• Yoichi Yoshimoto• Lead Coordinator

Rakuten Institute of TechnologyRakuten, Inc.

• So what’s my role? Connecting R&D projects to business and tech teams

Currently focusing on “Data Mining” and “Natural Language Processing” areas.

Rakuten, Inc.

1997 → 2014Our Mission: “Empowering

People and Societies through the Internet”.

Did you know…?

Key figures(1)

Key figures(2)

New Year

3.11

female （ PC）

w/o login （ PC ）

male （ SP）

female （ SP）

w/o login （ SP ）

male （ PC ）

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

So how do we utilize this data?

Visualizing

HITOKE Application

Analyzing (and grasping trends)

分析

【 Keyword Trend 】 Peak Season Identification

When is the peak season for selling school bags?

【 Keyword Trend 】 Peak Season Identification

Re-discovering peak season from time series data

Jan

. 1

st

Dec.

31

st

“School Bags” have 2 peak seasonsGrandparents start looking for school bags as gifts for theirgrandchildren after their visitduring summer vacation.

Maybe!

【 Keyword Trend 】 Discovering event related demands

Finding unknown co-relations from temporal keyword data

【 Keyword Trend 】 Discovering event related demands

Finding unknown co-relations from temporal keyword data

Source: empty - Philip Tautz

【 Keyword Trend 】 Discovering event related demands

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

Utilizing (for usability improvements)

ワンピース (one piece)

Item Genre SuggestionIdentifying Rakuten’s genres

which are related to user-input keywords

Item 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

Organizing (to acquire knowledge)

Attribute ExtractionItem pages in Rakuten are created by merchants

-> They Contain lots of unstructured text.

For better service, we need structured data.

Hard to see wine’s attributes Easy to see wine’s attributes

Attribute ExtractionItem pages in Rakuten are created by merchants

-> They Contain lots of unstructured text.

For better service, we need structured data.

Hard to see wine’s attributes Easy to see wine’s attributes

Attribute ExtractionWe can extract attributes regardless of categories or languages

as long as table data is available

Further Analyzing

GEAP: Global Event Analysis Platform• Collecting log data from any devices and services

with single platform.• Big data analysis of cross services