TY - JOUR
T1 - An OWA-based hierarchical clustering approach to understanding users’ lifestyles
AU - Nguyen, Jennifer
AU - Armisen, Albert
AU - Sánchez-Hernández, Germán
AU - Casabayó, Mònica
AU - Agell, Núria
N1 - Funding Information:
This research was supported by the INVITE research project ( TIN2016-80049-C2-1-R and TIN2016-80049-C2-2-R ), funded by the Spanish Ministry of Economy and Competitiveness .
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/2/29
Y1 - 2020/2/29
N2 - Based on users’ interactions with social networks, a method to understand users’ life-styles is developed. Descriptions of their lifestyles are obtained from previously reported experiences on these sites. Contextual information and contributed reviews lend insight into which elements are important for different lifestyles. In this paper, an ordered weighted averaging operator (OWA) is integrated with hierarchical clustering in order to find the similarity between users and clusters. Specifically, a two step measure is defined to compare and aggregate two clusters. To illustrate the efficiency of the methodology, a real case is implemented for 499 Yelp reviewers associated with 134,102 reviews across 11 variables and 373 Airbnb reviewers associated with 1,826 reviews across 14 variables.
AB - Based on users’ interactions with social networks, a method to understand users’ life-styles is developed. Descriptions of their lifestyles are obtained from previously reported experiences on these sites. Contextual information and contributed reviews lend insight into which elements are important for different lifestyles. In this paper, an ordered weighted averaging operator (OWA) is integrated with hierarchical clustering in order to find the similarity between users and clusters. Specifically, a two step measure is defined to compare and aggregate two clusters. To illustrate the efficiency of the methodology, a real case is implemented for 499 Yelp reviewers associated with 134,102 reviews across 11 variables and 373 Airbnb reviewers associated with 1,826 reviews across 14 variables.
KW - Clustering
KW - OWA
KW - Online reviews
UR - http://www.scopus.com/inward/record.url?scp=85076253160&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2019.105308
DO - 10.1016/j.knosys.2019.105308
M3 - Article
AN - SCOPUS:85076253160
SN - 0950-7051
VL - 190
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
M1 - 105308
ER -