This document summarizes a presentation about using online machine learning with Jubatus for a real estate search service called SUUMO. Toru Shimogaki from NTT DATA Corporation presented on how SUUMO uses Jubatus classifier and multidimensional scaling to learn users' preferences and provide personalized search results. The system presents users with two candidate properties and learns their tastes based on their selections. It combines Jubatus, which responds quickly to user clicks, with multidimensional scaling to map properties and allow efficient searching. The goal is to better serve first-time and mobile users by navigating searches and efficiently gaining their preferences.

1. GOTO Berlin Conference 2013
A use case of online machine learning
using Jubatus
2013/10/18
NTT DATA Corporation System Platforms Sector
OSS Professional Services
Toru Shimogaki
Copyright © 2013 NTT DATA Corporation

2. Who is Toru Shimogaki
 The Elephant Wizard as a team lead of “NTT DATA OSS
Professional Services”
 Deep and wide experience deploying Open Source
Software technologies for enterprise customers
10+ years
Contributor
Ex. pg_bulkload
Copyright © 2013 NTT DATA Corporation
6+ years
Leads Japanese
Hadoop Community
Co-Author
2nd Edition
in Japan
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3. About Jubatus (1/2)
 OSS Machine Learning Platform developed by NTT
Research Laboratories and Preferred Infrastructure, Inc
 Motivation: Take the Right Action at the Right Time, at
the Right Place
Copyright © 2013 NTT DATA Corporation
(Source : Hadoop Summit 2013)
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4. About Jubatus (2/2)
 Distributed Processing Framework and Streaming
Machine Learning Libraries
 Classification, Regression, Recommendation, Graph Mining,
Anomaly Detection
 Especially,
Jubatus Classifier has
small footprint and
responds with very low
latency. So it is easy
to scale for multiple
and simultaneous
requests.
(Source : Hadoop Summit 2013)
Copyright © 2013 NTT DATA Corporation
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5. Background
 “SUUMO” : Online Service for Real-Estate Business
Improve usability for smartphone access
Navigate those who don’t know how to search
residences
Efficiently approach the first time customer and
non regular short-time access users
Copyright © 2013 NTT DATA Corporation
5

6. “SUUMO” Real-Estate Service : Customer Reach and Brand Awareness
Compared to our competitors , ”SUUMO” has the largest customer reach and has scored highest in
brand awareness.
Customer reach (Unique Users)
(Million)
Brand awareness
(%)
9.9
64.3
5.2
4.7
2.6
4.7
SUUMO
A
Real estate
B
C
SUUMO
4.4
9.2
A
B
C
Aided SUUMO awareness
SUUMO UU
77.2%
9.9million /month
Awareness of Character 【SUUMO】
2013 RECRUIT SUMAI CO., LTD All Rights Reserved
87.6%
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7. Background
 “SUUMO” : Online Service for Real-Estate Business
Improve usability for smartphone access
Navigate those who don’t know how to search
residences
Efficiently approach the first time customer and
non regular short-time access users
Copyright © 2013 NTT DATA Corporation
7

8. Web service for SmartPhone (beta ver.)
 Repeatedly present two choices for candidates, then
system learns user’s flavor
Existing search service
simply list candidates with
filtering..
Resulted
recommendation
Present two typical
and salient choices.
Then a user simply
choose more
preferable one.
Present recommendation
based on acquired
user’s preference
Ask 10 times
with countdown
Copyright © 2013 NTT DATA Corporation
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9. Inside this SUUMO Web service
 This SUUMO web services is implemented with the
combination of some algorithms
 Multidimentional Scaling (MDS)
 Jubatus classifier (Passive Aggressive algorithm)
 etc.
Copyright © 2013 NTT DATA Corporation
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10. Building search space by MDS
 Building the search spaces for each station by Multidimensional
Scaling (MDS)
 daily batch processing using R
 Goal : to achieve O(log n) search (like binary search)
 Using MDS, convert multi dimensional vectors to lower dimensional one
with keeping distance relations among houses
Rent
Fraction on foot
Size
Deposit
Age of a building
...
Copyright © 2013 NTT DATA Corporation
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11. Learning user’s flavor using Jubatus
 Classify user’s flavor for real-estate using Jubatus
 Goal : reflect user action to result on real-time (cannot by R)
 Using Passive Aggressive algorithm
 If score of the area becomes lower than threshold, remove it from search area
 Processing this classification with low latency, and easy to scale
Initial state
1 clicked
2 clicked
6 clicked
10 clicked
 Using this approach :
 Keep to include discrete candidate, which is excluded in MDS space search but a
user still have some interest with unexpected attributions.
 Keep sufficient diversion in order to present “salient” candidate. It is necessary to
have distant choices for estimating user’s preference without losing features
Copyright © 2013 NTT DATA Corporation
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12. Wrap up
 Introduced a use case of online machine learning using Jubatus
 Now beta service for smartphone is released on SUUMO, which
is one of the largest residence service in Japan.
 Today I explained
 Building a Multi-dimensional Scaling search space
 Using Jubatus classifier to understand users preference adaptively
 Furute works
 Learn from similar users activity using logs
 Semantic analysis of sentences input by client
Copyright © 2013 NTT DATA Corporation
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13. NTT DATA Corporation System Platforms Sector
OSS Professional Services
URL:
http：//oss.nttdata.co.jp/hadoop/
mail： hadoop@kits.nttdata.co.jp
Copyright © 2013 NTT DATA Corporation