Research on Recommendation System Selection Based on Fuzzy VIKOR with Multiple Distances

Article Preview

Abstract:

Nowadays, there are several different kinds of methodology in selecting recommendation systems (CRS), and every method has its own evaluation criteria to pick up the best one. In this paper, a new MCDM method for recommendation system selection based on fuzzy VIKOR with multiple distances is introduced. It selects the best system by calculating values using three different distance calculation methods, which are Hamming distance, Euclidean distance and Hausdorff distance, and voting via Condorcet method. It minimizes the effect of distance and offers a more objective result than other methods and helps enterprises to select the most suitable recommendation system.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

481-485

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23(3), 329-342.

DOI: 10.1016/s0957-4174(02)00052-0

Google Scholar

[2] Tsaur, S. H., Chang, T. Y., & Yen, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM. Tourism management, 23(2), 107-115.

DOI: 10.1016/s0261-5177(01)00050-4

Google Scholar

[3] Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514-529.

DOI: 10.1016/j.ejor.2006.01.020

Google Scholar

[4] San Cristóbal, J. R. (2011). Multi-criteria decision-making in the selection of a renewable energy project in Spain: the Vikor method. Renewable energy, 36(2), 498-502.

DOI: 10.1016/j.renene.2010.07.031

Google Scholar

[5] Opricovic, S. (2011). Fuzzy VIKOR with an application to water resources planning. Expert Systems with Applications, 38(10), 12983-12990.

DOI: 10.1016/j.eswa.2011.04.097

Google Scholar

[6] Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.

DOI: 10.1016/s0377-2217(03)00020-1

Google Scholar

[7] Herrera, F., & Martinez, L. (2000).

Google Scholar

[8] Herrera, F. (2012). An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges. Information Sciences, 207, 1-18.

DOI: 10.1016/j.ins.2012.04.025

Google Scholar

[9] Forney Jr, G. (1966). Generalized minimum distance decoding. Information Theory, IEEE Transactions on, 12(2), 125-131.

DOI: 10.1109/tit.1966.1053873

Google Scholar

[10] Karsak, E. E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research, 40(13), 3167-3181.

DOI: 10.1080/00207540210146062

Google Scholar

[12] Sánchez, L., Couso, I., & Casillas, J. (2007, April). Modeling vague data with genetic fuzzy systems under a combination of crisp and imprecise criteria. In Computational Intelligence in Multicriteria Decision Making, IEEE Symposium on (pp.30-37.

DOI: 10.1109/mcdm.2007.369413

Google Scholar