Recommender systems using support vector machines

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Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems - content-based recommending and collaborative filtering (CF). This study focuses on improving the performance of recommender systems by using data mining techniques. This paper proposes an SVM based recommender system. Furthermore this paper presents the methods for improving the performance of the SVM based recommender system in two aspects: feature subset selection and parameter optimization. GA is used to optimize both the feature subset and parameters of SVM simultaneously for the recommendation problem. The results of the evaluation experiment show the proposed model's improvement in making recommendations.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2005
Language
English
Article Type
Article; Proceedings Paper
Keywords

NETWORKS

Citation

WEB ENGINEERING, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN COMPUTER SCIENCE, v.3579, pp.387 - 393

ISSN
0302-9743
URI
http://hdl.handle.net/10203/87604
Appears in Collection
MT-Journal Papers(저널논문)
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