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 (Germany)
Issue Date
2005
Citation

Web Engineering, 5th International Conference(ICWE 2005), Sydney, Australia, 27-29 July 2005, pp. 387-393(7)

ISSN
1611-3349
DOI
10.1007/11531371_50
URI
http://hdl.handle.net/10203/3799
Link
http://www.springerlink.com/content/5y3x9bjv74t6g0r2/?p=860260235e5e40b2b7813f87e3732654&pi=0
Appears in Collection
KGSF-Conference Papers(학술회의논문)

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