Social platform based interval valued intuitionistic fuzzy location recommendation system

Öner S. C., Öztayşi B., Öner M.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, vol.38, no.1, pp.1027-1042, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.3233/jifs-179466
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1027-1042
  • Istanbul Technical University Affiliated: Yes


The improvements in mobile technologies led to the wide adaptation and triggered the demand for location based services. In this respect, examining user similarities enable the analysis of user interests in terms of the determination of purchasing preferences and actual needs. User similarities are generally extracted from consumer life style, demographical information or the reflections from previously sent messages. In spite of the fact that these factors may not directly influence the purchasing decision, uncertain or lack of information can be encountered while establishing recommendation systems. Thus, researchers try to search other indicators that can reflect customer characteristics from spatial data, digital contribution in social media and search history for preferable representation of the changes in purchasing tendency. In this study, social platform based interval valued intuitionistic fuzzy location recommendation system is proposed by considering three common social platforms: Trip Advisor, Zomato and Foursquare. To perform restaurant offers to appropriate social platform users, a sentiment analysis is conducted to selected restaurants and number of negative, positive and neutral comments are extracted. After that, restaurant and location information are examined by using user, restaurant and location clustering via fuzzy clustering. Finally, intuitionistic fuzzy similarity matrix based collaborative filtering is used for restaurant offers to similar users.