Presentation 2010-11-19
Web page recommendation based on user's tagging tendency in social bookmark
Yuuki KISHIBATA, Takashi ISHIDA, Masayuki GOTO,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Recently, web page recommendation systems by using social bookmark services have been studied with development of information technology. Tags in the social bookmark sites are terms in which users are interested and the information of tags may be useful for recommendation purposes. There are various ways between users how to use tags and make a vombination of tags to a web page. In this study, the group of web pages which have the same tag is supposed to represent user's interests. Several variations of recommendation methods using the tags labeled by users are considered and the simulation experiments are shown in this paper. A case study by using the Hatena bookmark which is famous site in Japan is conducted to investigate the performances and properties of the recommendation models.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Social Bookmark / tag / recommendation
Paper # AI2010-39
Date of Issue

Conference Information
Committee AI
Conference Date 2010/11/12(1days)
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Paper Information
Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Web page recommendation based on user's tagging tendency in social bookmark
Sub Title (in English)
Keyword(1) Social Bookmark
Keyword(2) tag
Keyword(3) recommendation
1st Author's Name Yuuki KISHIBATA
1st Author's Affiliation Major in Industrial and Management Systems Engineering, Graduate School of Creative Science and Engineering, Waseda University()
2nd Author's Name Takashi ISHIDA
2nd Author's Affiliation Media Network Center, Waseda University
3rd Author's Name Masayuki GOTO
3rd Author's Affiliation Department of Industrial and Management Systems Engineering, School of Creative Science and Engineering, Waseda University
Date 2010-11-19
Paper # AI2010-39
Volume (vol) vol.110
Number (no) 301
Page pp.pp.-
#Pages 5
Date of Issue