Takashi Umeda is a researcher interested in recommendation algorithms, information diffusion on the internet, and consumer behavior in electronic commerce. He was born in Gifu Prefecture in 1985 and studied at Shizuoka University from 2003-2007 and the Tokyo Institute of Technology from 2007-2009. His bachelor's thesis examined direct financing in Silicon Valley using agent-based modeling and his master's thesis evaluated collaborative filtering using agent-based simulation in light of market environments. His research aims to design recommendation algorithms for electronic commerce by modeling interaction with market environments and evaluating collaborative filtering techniques.

1. Self-Introduction
name Takashi UMEDA
biography 1985 born in gifu pref.
2003~2007 Faculty of Informatics, Shizuoka Univ.
2007~2009 Deguchi Lab, Dep.of Computational
Intelligence and Systems Science, Tokyo Institute
Of Techology
Research Interest Recommendation algorithms,
Information diffusion on the Internet,
Consumer behavior on EC,
Bachelor’s thesis The research into direct financing in Silicon Valley
by Agent Based Modeling
master's thesis Evaluation of Collaborative Filtering by Agent-Based
Simulation in the light of a market environment
URL http://degulab.cs.dis.titech.ac.jp/~umeda07/
1
Deguchi Lab. http://degulab.cs.dis.titech.ac.jp/~umeda07/

2. Research Interest1/3 (Objective)
– なぜ協調フィルタリング
か?： Recommendation:
Evaluating and
• 最もポピュラー
Collaborative Filtering
• 特にユーザベース手法
designing the
– なぜABSか？:
に焦点
• Amazon.com
•
recommendation
– 分析内容：
市場構造を考慮可能
• user base CF
• ネイバー数と市場構造
algorithm on EC in the
•
の関係
ネイバー数：パラメータ Method: ABS
の1つ
light of the market • It is possible to model the
interaction with market
environments environment and recommendation
It is able to give some useful knowledge about
the recommendation for people who are
Objectives
engaged in web marketing, web system or EC
business.
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Deguchi Lab. http://degulab.cs.dis.titech.ac.jp/~umeda07/

3. Research Interest 2/3 (model)
Each item sold in the EC Website
Consumer belongs to one category
model consumer buy
Each consumer
has some recommend
parameters that EC Site
are used to decide buy • item-1(category1）
what he buys. consumer • item-2(category2)
recommend • item-3(category3)
buy
consumer recommend
Recommendation
model
Collaborative Filtering
(k-nearest neighbor )
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Deguchi Lab. http://degulab.cs.dis.titech.ac.jp/~umeda07/

4. Resarch Interest 3/3(Implement)
Data File (.CSV) External Object (.Java)
Consumer’s Consumer’s decision
preference SOARS(.vml)
Consumer Agent making
Item’s attribute
EC Spot
Collaborative
Sinario
Filtering
Execute! The simulation
is executed on
the Grid
machines.
Result
4
Deguchi Lab. http://degulab.cs.dis.titech.ac.jp/~umeda07/

5. Research Interest 4/4(Conclusion)
I’m performing the
experiments very hard
now!
5
Deguchi Lab. http://degulab.cs.dis.titech.ac.jp/~umeda07/