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1.Corporate Profile

2.Product Introductions

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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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Members

CEO: Toru NishikawaComputer Science, University of TokyoACM International Collegiate Programming Contest 2006 19th place

CTO: Kazuki OhtaComputer Science, University of TokyoACM International Collegiate Programming Contest 2006 13th place

Fellow: Daisuke OkanoharaComputer Science, University of TokyoMITOH 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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Technologies

Information Retrieval

Recommendation

Natural Language Processing

Machine Learning

Data Compression

Database System

Very Large Distributed System

Bioinformatics

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Basic Technologies

Academic Researches

Products

Services

Goals

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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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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1.Corporate Profile

2.Product Introductions

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Products & Categories

Search

Recommendation

Ad Network

Ohtaka : Collaborative Filtering

UbiMatch : Ad Network System

reflexa : Association Search

Sedue 24 : Full-Text Search

Sedue Flex : Approximate Search

Hotate : Content-Based Filtering

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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 index100% recall ratio

Linear Scale-UpVerified up to 128 threads on a Sun box

Easy Scale-OutIndexer and searcher nodes can be added without system stop.

High ReliabilityCustomizable Ranking Feature

ApplicationWeb SearchDocument Search, Enterprise SearchText Mining

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Sedue 24

SSD CapabilityNew 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

DemonstrationSearch from Wikipedia data for ALL LANGUAGES (about 50GB)

http://demo.sedue.org/wikipediasearch/

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Sedue 24 Case Study

The third largest mobile search portal in

Japan4,000,000 Unique Users / month

Mobile web search function is based on

Sedue 24

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Sedue 24 Case Study

The largest social bookmark service in

Japan216,000 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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Sedue Flex

High-performance approximate search engineExtends the complete matching technology of Sedue 24 to approximate matching.

Allowing mismatches and gapsUltrafast speed enabled by the latest algorithm.

A few milliseconds to seconds response time with allowing 10 to 20% errors to search gigabytes dataSedue Flex Plus (option) and additional memory consumption enables 10 to 30 times faster speed

ApplicationGenome Analysis (several times – several hundred times faster than BLAST)Analysis of noisy data (voice, video...)

CasesResearch InstituteMedical University

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reflexa

Associative search enginereflexa 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 accuracydegree of association among words is precisely calculated reflexa mechanically learns a lot of documents and calculates degrees of association among words precisely.

High performanceAssociation 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‟

ApplicationEncyclopedia searchBrainstorming supporting tool

Demonstration (Japanese)http://labs.preferred.jp/reflexa/

検索

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reflexa Case Studies

Ashi@A service to track access history over multiple BLOG services.

Reflexa is used to recommend similar blogs to users.

Hatena BookmarkThe largest social bookmark service in Japan.

Reflexa is used to search web pages that have similar contents.

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Hotate

Article recommendation engineAn 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 indexing10 seconds to index 20,000 documents

Easy to tuneScore 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

ApplicationNews sitesBibliographic retrieval

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Hotate Case Study

Online news site provided by Asahi

Shimbun, which is a Japanese high

quality paper company10,500,000 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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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: 7.5GBHDD: 850GBOS: Fedora Core 6 (x86_64)

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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.

ApplicationE-commerce sites

Word-of-mouth marketing, CGM services

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UbiMatch

Ad-network system for mobileAutomatic 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 modelsServiced by Preferred Infrastructure itself

OEM

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UbiMatch – system model

UbiMatch

Media Advertiser

PortalGame

E-Commerce

Online Book

Blog

News

UbiMatch

OEM

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Feel free to contact us for more

detailed information!

Preferred Infrastructure Inc,

Email info@preferred.jp

TEL +81-3-6662-8675 (Tokyo Japan)