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http://dx.doi.org/10.5391/JKIIS.2011.21.5.624

A New Kernelized Approach to Recommender System  

Lee, Jae-Hun (연세대학교 전기전자공학부)
Hwang, Jae-Pil (현대자동차그룹)
Kim, Eun-Tai (연세대학교 전기전자공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.5, 2011 , pp. 624-629 More about this Journal
Abstract
In this paper, a new kernelized approach for use in a recommender system (RS) is proposed. Using a machine learning technique, the proposed method predicts the user's preferences for unknown items and recommends items which are likely to be preferred by the user. Since the ratings of the users are generally inconsistent and noisy, a robust binary classifier called a dual margin Lagrangian support vector machine (DMLSVM) is employed to suppress the noise. The proposed method is applied to MovieLens databases, and its effectiveness is demonstrated via simulations.
Keywords
Recommender system; Dual margin Lagrangian SVM; MovieLens;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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