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A Two-Step Best-Worst Method (BWM) and K-Means Clustering Recommender System Framework

In: Advances in Best-Worst Method

Author

Listed:
  • Saeed Najafi-Zangeneh

    (Amirkabir University of Technology)

  • Naser Shams-Gharneh

    (Amirkabir University of Technology)

  • Ali Arjomandi-Nezhad

    (Amirkabir University of Technology)

Abstract

Finding a suitable item among thousands or even millions of items on e-commerce websites is a cumbersome task. Recommender systems are designed as a solution to this challenge. A decent recommender system helps the customers to find items matching their taste and preferences. This paper suggested that the clusters of multi-criteria decision-making (MCDM) weights can be used as a representation for the diversity of priorities in society. The weights are computed using the Best-Worst Method (BWM). A customer is assigned to a cluster of weights based on his/her former orders. During the next step, the probability of buying an item is computed. It has been discussed why the proposed model is suitable for real-world recommender systems. A general guidance on practical implementation is also provided. A case study of fifty-nine students on their preferred criteria of mobile and laptop will be analyzed to investigate the validation of the framework.

Suggested Citation

  • Saeed Najafi-Zangeneh & Naser Shams-Gharneh & Ali Arjomandi-Nezhad, 2022. "A Two-Step Best-Worst Method (BWM) and K-Means Clustering Recommender System Framework," Lecture Notes in Operations Research, in: Jafar Rezaei & Matteo Brunelli & Majid Mohammadi (ed.), Advances in Best-Worst Method, pages 29-40, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-89795-6_3
    DOI: 10.1007/978-3-030-89795-6_3
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