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

Saeed Najafi-Zangeneh (), Naser Shams-Gharneh and Ali Arjomandi-Nezhad
Additional contact information
Saeed Najafi-Zangeneh: Amirkabir University of Technology
Naser Shams-Gharneh: Amirkabir University of Technology
Ali Arjomandi-Nezhad: Amirkabir University of Technology

A chapter in Advances in Best-Worst Method, 2022, pp 29-40 from Springer

Abstract: 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.

Keywords: Best Worst Method; Clustering; Recommender systems (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-89795-6_3

Ordering information: This item can be ordered from
http://www.springer.com/9783030897956

DOI: 10.1007/978-3-030-89795-6_3

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().