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Hironori Tomobe

dblp:96/6271 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4Graphics, computer vision, multimedia, augmented reality and games · 2Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Web and social media mining · 77% Information retrieval · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Web and social media mining
social network extraction
0.112007
Robust Estimation of Google Counts for Social Network Extraction · AAAI 2007
Information retrieval
web search
0.012007
Robust Estimation of Google Counts for Social Network Extraction · AAAI 2007

Methods — techniques the papers use, named apart from their topics

robust estimation · 0.1google counts · 0.1
YearPublicationVenuePosition
2009 Public Large Screen Enabled Content Collection and Connection
Kosuke Numa, Hironori Tomobe, Tatsuo Sugimoto, Masako Miyata, Kiyoko Toriumi, Jun Abe, Koichi Hori
KES (2)2
2007 Robust Estimation of Google Counts for Social Network Extraction
Yutaka Matsuo, Hironori Tomobe, Takuichi Nishimura
AAAI2
2004 Finding Social Network for Trust Calculation
Yutaka Matsuo, Hironori Tomobe, Kôiti Hasida, Mitsuru Ishizuka
ECAI2
2003 Mining Social Network of Conference Participants from the Web
abstract
In a ubiquitous computing environment, it is desirable to provide a user with information depending on a user's situation, such as time, location, user behavior, and social context. At conventions, such as academic conferences and exhibitions, where participants must register in advance, the social context of participants can be extracted from the Web using their names and affiliations without asking the participants many questions. Here, we attempt to extract the social network of participants from the Web, where a node represents a participant and an edge represents the relationship of two participants. Each edge is added using the number of pages retrieved by a search engine which include both participants names. Moreover, each edge has a label such as "coauthors" and "members of the same project" by applying classification rules to the page content. We show an example of the extracted network and make a preliminary evaluation. This network can be used in many information services, such as finding an appropriate introducer or negotiator, and who one should talk to in order to efficiently expand his/her network.
Yutaka Matsuo, Hironori Tomobe, Kôiti Hasida, Mitsuru Ishizuka
Web Intelligence2