Intelligent Electronic Commerce Service Based on Understanding of User Behaviors
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Transcript of Intelligent Electronic Commerce Service Based on Understanding of User Behaviors
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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/
12
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
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Thank you!!