Social platform based interval valued intuitionistic fuzzy location recommendation system

Sultan Ceren Oner*, Başar Oztaysi, Mahir Oner

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

5 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)1027-1042
Sayfa sayısı16
DergiJournal of Intelligent and Fuzzy Systems
Hacim38
Basın numarası1
DOI'lar
Yayın durumuYayınlandı - 2020

Bibliyografik not

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© 2020 - IOS Press and the authors. All rights reserved.

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