Corporate profile and product summary of Preferred Infrastructure Inc,

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2. 1.Corporate Profile
2.Product Introductions
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3. Overview
Company Name Preferred Infrastructure Inc,
Foundation March 2006
CEO Toru Nishikawa
# of Employees 14
Location Tokyo, Japan
URL http://preferred.jp/ (Japanese Only)
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4. Members
CEO: Toru Nishikawa
Computer Science, University of Tokyo
ACM International Collegiate Programming Contest 2006 19th place
CTO: Kazuki Ohta
Computer Science, University of Tokyo
ACM International Collegiate Programming Contest 2006 13th place
Fellow: Daisuke Okanohara
Computer Science, University of Tokyo
MITOH Program(Exploratory IT Human Resources Program sponsored by IPA Japan)
A New Data Compression Algorithm using Word Extraction Method. (2002)
Universal Probabilistic Language Models (2003)
Document Classification using Context Information. (2004-2005)
Many of other members have also achieved outstanding
results at MITOH Program, ACM/ICPC and so on.
A team of front-line academic researchers and engineers
who can implement their outputs they created at a high level
of quality.
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5. Technologies
Information Retrieval
Recommendation
Natural Language Processing
Machine Learning
Data Compression
Database System
Very Large Distributed System
Bioinformatics
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6. Goals
Basic Technologies Products
Academic Researches Services
To put leading-edge research results in
the academic world to practical use as
soon as possible.
We challenge the most difficult problems
and provide solutions for them.
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7. Products & Services
Product Development & Licensing
Business
Search
Recommendation
Ad Network System
Ad Network Hosting
Cooperative research and development
with customers/partners based on our
unique and extensive technical
background
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8. 1.Corporate Profile
2.Product Introductions
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9. Products & Categories
Sedue 24 : Full-Text Search
Search Sedue Flex : Approximate Search
reflexa : Association Search
Hotate : Content-Based Filtering
Recommendation
Ohtaka : Collaborative Filtering
UbiMatch : Ad Network System
Ad Network
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10. Sedue 24
Scalable and high performance distributed full-
text search engine
The first commercial search engine in the world that is
based on Compressed Suffix Array method.
High performance on-memory search with a
compressed index
100% recall ratio
Linear Scale-Up
Verified up to 128 threads on a Sun box
Easy Scale-Out
Indexer and searcher nodes can be added without
system stop.
High Reliability
Customizable Ranking Feature
Application
Web Search
Document Search, Enterprise Search
Text Mining
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11. Sedue 24
SSD Capability
New index engine optimized for SSD
Index data is stored on SSD
Well-tuned based on characteristics of SSD
and system balance
Only one PC server is needed to search from
several hundreds GB of data
Demonstration
Search from Wikipedia data for ALL
LANGUAGES (about 50GB)
http://demo.sedue.org/wikipediasearch/
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12. Sedue 24 Case Study
The third largest mobile search portal in
Japan
4,000,000 Unique Users / month
Mobile web search function is based on
Sedue 24
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13. Sedue 24 Case Study
The largest social bookmark service in
Japan
216,000 registered users
3,500,000 UU/month, 7,900,000 PV/day
11,600,000 bookmarked URLs
50GB of HTML data without tags
34,000,000 bookmarks
40,000,000 tags
Sedue 24 enables web search for 10
million+ bookmarked web pages
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14. Sedue Flex
High-performance approximate search engine
Extends the complete matching technology of Sedue 24 to
approximate matching.
Allowing mismatches and gaps
Ultrafast speed enabled by the latest algorithm.
A few milliseconds to seconds response time with
allowing 10 to 20% errors to search gigabytes data
Sedue Flex Plus (option) and additional memory
consumption enables 10 to 30 times faster speed
Application
Genome Analysis (several times – several hundred times
faster than BLAST)
Analysis of noisy data (voice, video...)
Cases
Research Institute
Medical University
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15. 検索
reflexa
Associative search engine
reflexa accepts a set of words and searches associated words with
them
when you don‟t think of appropriate search words.
when you don‟t know what kind of information you are really
looking for.
High accuracy
degree of association among words is precisely calculated
reflexa mechanically learns a lot of documents and calculates
degrees of association among words precisely.
High performance
Association information is compressed and stored on memory
Applicable to other types of association but word-to-words
„Purchase history‟ to „recommended items‟
„Web browsing history‟ to „recommended web pages‟
Application
Encyclopedia search
Brainstorming supporting tool
Demonstration (Japanese)
http://labs.preferred.jp/reflexa/
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16. reflexa Case Studies
Ashi@
A service to track access history over multiple
BLOG services.
Reflexa is used to recommend similar blogs to
users.
Hatena Bookmark
The largest social bookmark service in Japan.
Reflexa is used to search web pages that have
similar contents.
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17. Hotate
Article recommendation engine
An ability to search associated documents quickly and
precisely.
A single Intel-based PC server can process more
than tens of millions of requests per month.
Automatic keyword extraction and fast indexing
10 seconds to index 20,000 documents
Easy to tune
Score that stands for the degree of association and
keywords that cause the association between 2
documents are explicitly displayed.
Manual adjustment
• e.g. not associate festival news with unhappy news
Application
News sites
Bibliographic retrieval
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18. Hotate Case Study
Online news site provided by Asahi
Shimbun, which is a Japanese high
quality paper company
10,500,000 Unique Users / month
4,000,000,000 Page Views / month
12,000 articles
Hotate is used to recommend related
news articles
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19. Hotate Case Study
Online IT news media provided by Nikkei
Business Publications, which is a major
business-oriented publisher
20,000,000 PV / month
Hotate is used to recommend related news articles.
Running on Amazon EC2
“Large Instance”($0.4 / hour)
2ECU * 2 cores (1ECU=Intel Xeon 1.0 – 1.2GHz)
Memory: 7.5GB
HDD: 850GB
OS: Fedora Core 6 (x86_64)
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20. Ohtaka
Rating-based recommendation engine
Ohtaka recommends items based on item rating
data given by users.
user preferences and item attributes are used
to forecast items that is expected to be highly
evaluated
A kind of collaborative filtering
Ability to accommodate more than millions of
users and items.
Application
E-commerce sites
Word-of-mouth marketing, CGM services
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21. UbiMatch
Ad-network system for mobile
Automatic optimization of ad delivery
Optimization based on content, user behavior
and user profile
Preferred Infrastructure‟s search,
recommendation and machine learning
technologies are applied.
Security facility for user privacy protection
Business models
Serviced by Preferred Infrastructure itself
OEM
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22. UbiMatch – system model
Advertiser
Media
Game
Portal
Blog UbiMatch E-Commerce
News Online Book
UbiMatch
OEM
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23. Feel free to contact us for more
detailed information!
Preferred Infrastructure Inc,
Email info@preferred.jp
TEL +81-3-6662-8675 (Tokyo Japan)
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