Genders prediction from indoor customer paths by Levenshtein-based fuzzy kNN

Dogan O., Öztayşi B.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.136, ss.42-49, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 136
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1016/j.eswa.2019.06.029
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.42-49
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Companies have an advantage over the competitors if they can present customized offers to customers. Demographic information of customers is critical for the companies to develop individualized systems. While current technologies make it easy to collect customer data, the main problem is that demographic data are usually incomplete. Hence, several methods are developed to predict unknown genders of customers. In this study, customer genders are predicted from their paths in a shopping mall using fuzzy sets. A fuzzy classification method based on Levenshtein distance is developed for string data that refer to the indoor customer paths. Although there are several ways to predict the gender, no study has focused on path-based gender classification. The originality of the research is to classify customer data into the gender classes using indoor paths. (C) 2019 Elsevier Ltd. All rights reserved.