(An) algorithmic framework of adaptive collaborative filtering for internet marketing인터넷 마케팅을 위한 적응적 협동필터링 알고리즘에 관한 연구

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Mass customization in internet marketing and the overwhelming amount of information to be processed each day lead internet users to a growing interest in collaborative recommendation systems which suggest products and services to users. Collaborative Filtering (CF) has been used successfully at e-commerce sites like Amazon.com and JCPenny. Despite its popularity, CF has some limitations. In traditional CF, all the ratings are weighted equal regardless of the error they contain. This is problematic, because each rating can have different level of error. In this paper, an adaptive collaborative filtering algorithm is proposed. The suggested algorithm captures the level of error that each rating contains, and utilizes the captured knowledge to increase the adaptability of the collaborative filtering recommendation. The concept of the adaptive weight proposed to express the relative informational value of each rating in numerical form. The performance of suggested algorithm is verified by conducting an experiment.
Advisors
Park, Sung-Jooresearcher박성주researcher
Description
한국과학기술원 : 경영공학전공,
Publisher
한국과학기술원
Issue Date
2001
Identifier
165964/325007 / 000993064
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 경영공학전공, 2001.2, [ vi, 48 p. ]

Keywords

CF; Collaborative Filtering; adaptive; 적응적; 추천; 협동필터링; recommendation

URI
http://hdl.handle.net/10203/53586
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=165964&flag=dissertation
Appears in Collection
KGSM-Theses_Master(석사논문)
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