Paper Info
Title
Correlation Measures for Bipolar Rating Profiles.
Abstract
We introduce new correlation measures for measuring similarity and association of rating profiles obtained from bipolar rating scales. Instead of the measurement based approach when the user's rating is considered as a number measured in ordinal, interval or ratio scales we use model based approach when user's rating is modeled by bipolar score function that can be nonlinear. This approach can use different models of preferences for different users. The values of utility function can be adjusted in machine learning procedure to obtain better solutions on the output of recommender or decision making system. We show that Pearson's correlation coefficient often used for measuring similarity between bipolar rating profiles in recommender systems has some drawbacks. New correlation measures proposed in the paper have not these drawbacks. These measures are obtained using general methods of construction of association measures from similarity measures on sets with involutive operation. Proposed measures can be used in recommender systems, in opinion mining and in sociological research for analysis of possible relationships between opinions of users and ratings of items.
Year
DOI
Venue
2017
10.1007/978-3-319-67137-6_3
FUZZY LOGIC IN INTELLIGENT SYSTEM DESIGN: THEORY AND APPLICATIONS
Keywords
Field
DocType
Rating scale,Bipolar scale,Recommender system,Opinion mining,Sentient analysis,Correlation,Association measure
Recommender system,Correlation coefficient,Nonlinear system,Sentiment analysis,Computer science,Ordinal number,Bipolar score,Rating scale,Correlation,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
648
2194-5357
1
PageRank 
References 
Authors
0.35
8
6
Name
Order
Citations
PageRank
Fernando Monroy-Tenorio110.35
Ildar Batyrshin2328.63
Alexander Gelbukh32843269.19
Valery Solovyev43810.57
Nailya Kubysheva510.35
Imre J. Rudas638863.89