The Use of Recommender Systems in Decision Support–A Case Study on Used Car Dealers
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The Use of Recommender Systems in Decision Support–A Case Study on Used Car Dealers

Authors: Nalinee Sophatsathit

Abstract:

This research focuses on the use of a recommender system in decision support by means of a used car dealer case study in Bangkok Metropolitan. The goal is to develop an effective used car purchasing system for dealers based on the above premise. The underlying principle rests on content-based recommendation from a set of usability surveys. A prototype was developed to conduct buyers- survey selected from 5 experts and 95 general public. The responses were analyzed to determine the mean and standard deviation of buyers- preference. The results revealed that both groups were in favor of using the proposed system to assist their buying decision. This indicates that the proposed system is meritorious to used car dealers.

Keywords: Recommender Systems, Decision Support, Content- Based Recommendation, used car dealer.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058119

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[1] Ana Belén Barrag├íns Mart├¡nez, José J. Pazos Arias, Ana Fern├índez Vilas, Jorge Garc├¡a Duque, and Mart├¡n L├│pez Nores. "What's on TV Tonight? An Efficient and Effective Personalized Recommender System of TV Programs." IEEE Transactions on Consumer Electronics, Vol. 55, No. 1, 2009, 286-294.
[2] Ekstrand, M. D., Kannan, P., Stemper, J.A., Butler, J. T., Konstan, J. A., and Riedl, J. T. "Automatically Building Research Reading Lists." ACM conference on Recommender systems (RecSys-10), 2010, 159-166.
[3] G. Adomavicius and Y. Kwon. "New Recommendation Techniques for Multicriteria Rating Systems." IEEE Intelligence Systems, vol. 22, No. 3, 2007, 48-55.
[4] Maneeroj, S. and Takasu, "A. Hybrid Recommender System Using Latent Features." Proceedings of the IEEE International Symposium on Mining and Web (MAW09), 2009, 661-666.
[5] Nascimento, C., Laender, A. H. F., Silba, A. S. D., and Goncalves, M. A. "A Source Independent Framework for Research Paper Recommendation." Joint Conference on Digital Libraries (JCDL11), 2011, 297-306.
[6] Olifur Pall E. "Content Personalization for Mobile TV Combining Content-Based and Collaborative Filtering." Master Thesis of Center for Information and Communication Technology (CICT), Technical University of Denmark (DTU), 2007.
[7] R. J. Mooney and L. Roy. "Content-Based Book Recommending Using Learning for Text Categorization." Proceedings of the Fifth ACM conference on Digital Libraries, 2007, 195-204.
[8] Robin van Meteren, Maarten van Someren. "Using Content-Based Filtering for Recommendation." Proceedings of ECML 2000 Workshop: Machine Learning in New Information Age, 2000, 47-56.
[9] First car tax exemption, Excise Department, Ministry of Finance, September 16, 2011.