Optimizing collaborative filtering recommender systems

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Collaborative filtering (CF) is the most successful recommendation technique, which has been used in a number of different applications. In traditional CF, the ratings of all items are equally weighted when similarity measure is calculated. But, if the importance of features (or items) is different respectively, feature weighting structure needs to be changed according to the importance of features. This paper presents a CA based feature weighting method. Through this weighting method, we can focus on the good items while removing bad ones or reducing their impacts.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2005
Language
English
Article Type
Article; Proceedings Paper
Citation

ADVANCES IN WEB INTELLIGENCE, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN COMPUTER SCIENCE, v.3528, pp.313 - 319

ISSN
0302-9743
URI
http://hdl.handle.net/10203/89962
Appears in Collection
MT-Journal Papers(저널논문)
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