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.
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References
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)
Cao, L., Tay, F.E.H.: Financial Forecasting Using Support Vector Machines. Neural Computing & Applications 10, 184–192 (2001)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)
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)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison- Wesley, New York (1989)
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)
Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)
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)
Joachims, T.: Text Categorization with Support Vector Machines, Technical report, LS VIII Number 23, University of Dormund (1997)
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)
Schafer, J.B., Konstan, J.A., Riedl, J.: Electronic Commerce Recommender Applications. Data Mining and Knowledge Discovery 5(1/2), 115–153 (2001)
Schmidt, M.S.: Identifying Speaker with Support Vector Networks. In: Proceedings of Interface 1996, Sydney (1996)
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)
Sun, Z., Bebis, G., Miller, R.: Object Detection using Feature Subset Selection. Pattern Recognition 27, 2165–2176 (2004)
Tang, K.S., Man, K.F., Kwong, S., He, Q.: Genetic Algorithms and Their Applications. IEEE Signal Processing Magazine 13, 22–37 (1996)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
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Min, SH., Han, I. (2005). Recommender Systems Using Support Vector Machines. In: Lowe, D., Gaedke, M. (eds) Web Engineering. ICWE 2005. Lecture Notes in Computer Science, vol 3579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11531371_50
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DOI: https://doi.org/10.1007/11531371_50
Publisher Name: Springer, Berlin, Heidelberg
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