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Fuzzy Multi-Criteria Decision Making and Fuzzy Information Gain Based Automotive Recommender System

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Fuzzy Logic in Intelligent System Design (NAFIPS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 648))

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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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Notes

  1. 1.

    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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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Bhagwan Parshuram Institute of Technology, Delhi, India

    Charu Gupta

  2. Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India

    Amita Jain

Authors
  1. Charu Gupta
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  2. Amita Jain
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Corresponding author

Correspondence to Charu Gupta .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Reformat

  5. Laboratory of Computational Intelligence and Automation, University of Waterloo, Waterloo, Ontario, Canada

    William Melek

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67136-9

  • Online ISBN: 978-3-319-67137-6

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