中文版 | English
Title

An optimization of collaborative filtering personalized recommendation algorithm based on time context information

Author
DOI
Publication Years
2015
ISSN
1868-4238
EISSN
1868-422X
Source Title
Volume
449
Pages
146-155
Abstract
This paper proposes an improved collaborative filtering algorithm based on time context information. Introducing the time information into the traditional collaborative filtering algorithm, the essay studies the changes of user preference in the time dimension. In this paper the time information includes three aspects: the time context information; the interest decays with the time; items similarity factor. This paper first uses Pearson correlation coefficient calculates time context similarity, pre-filtering the time-context. Through the experiment, the improved algorithm has higher accuracy than the traditional filter algorithms without time factor in the TOP-N recommendation list. It proves that time-context information of user's can affect the user's preference.
Keywords
SUSTech Authorship
Others
Language
English
URL[Source Record]
Scopus EID
2-s2.0-84925240376
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/382662
DepartmentSouthern University of Science and Technology
Affiliation
1.School of Information Management and Engineering,Shanghai University of Finance and Economics,Shanghai,China
2.South University of Science and Technology of China,Shenzhen,China
3.School of Systems Engineering,University of Reading,Whiteknights,Reading,United Kingdom
Recommended Citation
GB/T 7714
Jin,Xian,Zheng,Qin,Sun,Lily. An optimization of collaborative filtering personalized recommendation algorithm based on time context information[C],2015:146-155.
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