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Recommender Systems Using Support Vector Machines

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

Abstract

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.

Keywords

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.

References

  1. 1.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorighms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 43–52 (1998)Google Scholar
  2. 2.
    Cao, L., Tay, F.E.H.: Financial Forecasting Using Support Vector Machines. Neural Computing & Applications 10, 184–192 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)zbMATHGoogle Scholar
  4. 4.
    Fan, A., Palaniswami, M.: Selecting Bankruptcy Predictors Using A support Vector Machine Approach. In: Proceeding of the International Joint Conf. on Neural Network, vol. 6, pp. 354–359 (2000)Google Scholar
  5. 5.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison- Wesley, New York (1989)zbMATHGoogle Scholar
  6. 6.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings on the ACM 2000 Conference on Computer Supported Cooperative Work, Philadelphia, pp. 241–250 (2000)Google Scholar
  7. 7.
    Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  8. 8.
    Huang, Z., Che, H., Hsu, C.-J., Chen, W.-H.: Credit rating analysis with support vector machines and neural networks: a Market comparative study. Decision Support Systems 37, 543–558 (2004)CrossRefGoogle Scholar
  9. 9.
    Joachims, T.: Text Categorization with Support Vector Machines, Technical report, LS VIII Number 23, University of Dormund (1997)Google Scholar
  10. 10.
    Sarwar, B.M., Konstan, J.A., Borchers, A., Herlocker, J.L., Miller, B.N., Ried1, J.: Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In: Proceedings of CSCW 1998, Seattle, WA (1998)Google Scholar
  11. 11.
    Schafer, J.B., Konstan, J.A., Riedl, J.: Electronic Commerce Recommender Applications. Data Mining and Knowledge Discovery 5(1/2), 115–153 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Schmidt, M.S.: Identifying Speaker with Support Vector Networks. In: Proceedings of Interface 1996, Sydney (1996)Google Scholar
  13. 13.
    Sclkopf, B., Burges, C., Vapnik, V.: Extracting support data for a given task. In: Fayyad, U.M., Uthurusamy, R. (eds.) Proceedings of First International Conference on Knowledge Discovery & Data Mining. AAAI Press, Menlo Park (1995)Google Scholar
  14. 14.
    Sun, Z., Bebis, G., Miller, R.: Object Detection using Feature Subset Selection. Pattern Recognition 27, 2165–2176 (2004)CrossRefGoogle Scholar
  15. 15.
    Tang, K.S., Man, K.F., Kwong, S., He, Q.: Genetic Algorithms and Their Applications. IEEE Signal Processing Magazine 13, 22–37 (1996)CrossRefGoogle Scholar
  16. 16.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  17. 17.

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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