Abstract
In the current scenario, everyone is very possessive to buy the most suitable automobile for them. The choice to buy an automobile is governed by a large number of features like budget/price, mileage, exteriors, interiors, security features and so on. In this paper an automotive recommender system is proposed which uses the multidimensional criteria to select the best alternatives from a large pool of choices. In this paper, firstly, a feature vector is constructed for each automobile; secondly, a fuzzy information gain is computed for each criteria. This fuzzy gain is used as the weight of the criteria in fuzzy multidimensional decision making. Thus, the choice of automobiles in descending order of preference is recommended.
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Frankfurt Motor Show: Findings by OICA, Published by Kim Hjelmgaard, USA TODAY on Sept. 16, 2015 http://www.usatoday.com/story/money/cars/2015/09/16/survey-people-cant-imagine-life-without-cars/32489283/.
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Gupta, C., Jain, A. (2018). Fuzzy Multi-Criteria Decision Making and Fuzzy Information Gain Based Automotive Recommender System. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_30
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DOI: https://doi.org/10.1007/978-3-319-67137-6_30
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