PFI Corporate Profile

1.Corporate Profile 2.Product Introductions 2

Overview Company Name Preferred Infrastructure Inc, Foundation March 2006 CEO Toru Nishikawa # of Employees 14 Location Tokyo, Japan URL http://preferred.jp/ (Japanese Only) 3

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. 4

Technologies Information Retrieval Recommendation Natural Language Processing Machine Learning Data Compression Database System Very Large Distributed System Bioinformatics 5

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. 6

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 7

1.Corporate Profile 2.Product Introductions 8

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 9

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 10

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/ 11

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 12

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 13

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 14

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/ 15

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. 16

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 17

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 18

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) 19

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 20

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 21

UbiMatch – system model Advertiser Media Game Portal Blog UbiMatch E-Commerce News Online Book UbiMatch OEM 22

Feel free to contact us for more detailed information! Preferred Infrastructure Inc, Email info@preferred.jp TEL +81-3-6662-8675 (Tokyo Japan) 23

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PFI Corporate Profile

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