A Study on Personalized Recommendation Method Based on Contents Using Activity and Location Information (이용자 이용행위 및 콘텐츠 위치정보에 기반한 개인화 추천방법에 관한 연구)

KIM YONG (김용); Mun-Seok Kim (김문석); Yoon-Beom Kim (김윤범); Jae-Hong Park (박재홍)

doi:10.3743/KOSIM.2009.26.1.081

A Study on Personalized Recommendation Method Based on Contents Using Activity and Location Information

Journal of the Korean Society for Information Management
Abbr : JKOSIM
2009, 26(1), pp.81~105
DOI : 10.3743/KOSIM.2009.26.1.081
Publisher : 한국정보관리학회
Research Area : Interdisciplinary Studies > Library and Information Science
Received : February 12, 2009
Accepted : February 26, 2009
Published : March 30, 2009

KIM YONG ¹, Mun-Seok Kim ², Yoon-Beom Kim ³, Jae-Hong Park ⁴

¹전북대학교
²전라북도 교육청
³전북대학교 문헌정보학과
⁴(주) 유라클

Accredited

ABSTRACT

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.

KEYWORDS

IPTV, personalization, recommendation, collaborative filtering, contents-based recommendation

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* References for papers published after 2023 are currently being built.

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Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Study on Personalized Recommendation Method Based on Contents Using Activity and Location Information

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Citation status

* References for papers published after 2023 are currently being built.

Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31

A Study on Personalized Recommendation Method Based on Contents Using Activity and Location Information

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.