Recommender Systems Using Support Vector Machines

  • Sung-Hwan Min
  • Ingoo Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3579)


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.


Support Vector Machine Recommender System Feature Subset Collaborative Filter Feature Subset Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sung-Hwan Min
    • 1
  • Ingoo Han
    • 1
  1. 1.Graduate School of ManagementKorea Advanced Institute of Science and TechnologySeoulKorea

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