TY - JOUR
T1 - Social platform based interval valued intuitionistic fuzzy location recommendation system
AU - Oner, Sultan Ceren
AU - Oztaysi, Başar
AU - Oner, Mahir
N1 - Publisher Copyright:
© 2020 - IOS Press and the authors. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - interval valued intuitionistic fuzzy sets
KW - Location based systems
KW - recommendation systems
UR - http://www.scopus.com/inward/record.url?scp=85078348158&partnerID=8YFLogxK
U2 - 10.3233/JIFS-179466
DO - 10.3233/JIFS-179466
M3 - Article
AN - SCOPUS:85078348158
SN - 1064-1246
VL - 38
SP - 1027
EP - 1042
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 1
ER -