Title
Consumer preference prediction by using a hybrid evidential reasoning and belief rule-based methodology
Abstract
Consumer preference prediction is a key factor to the success of new product development. This paper presents a hybrid evidential reasoning (ER) and belief rule-based (BRB) methodology for consumer preference prediction and a novel application to orange juices. The orange juices are distinguished by their values of sensory attributes, which are grouped for simplicity into different categories such as appearance, aroma, texture, flavour, and aftertaste. The ER approach is used to aggregate consumer preferences for category attributes into an overall preference, and the BRB methodology is used to model the casual relationships between category attributes and their sensory attributes. The casual relationships between the overall preference and the sensory attributes of orange juices are trained and tested using real data and memorized for prediction or new product design. A case study involving 16 orange juices is conducted using the proposed hybrid ER and BRB methodology to demonstrate its novel applications. The results show that the hybrid ER and BRB methodology can fit and predict consumer preferences with high accuracy.
Year
DOI
Venue
2009
10.1016/j.eswa.2008.10.052
Expert Syst. Appl.
Keywords
Field
DocType
consumer preference mapping,sensory attribute,consumer preference prediction,category attribute,prediction accuracy,hybrid evidential reasoning,er approach,overall preference,evidential reasoning,belief rule base,belief rule-based methodology,brb methodology,novel application,aggregate consumer preference,orange juice,casual relationship,new product development,rule based
Data mining,Rule-based system,Computer science,Artificial intelligence,Casual,Evidential reasoning approach,Aftertaste,Machine learning,New product development
Journal
Volume
Issue
ISSN
36
4
Expert Systems With Applications
Citations 
PageRank 
References 
18
0.61
5
Authors
4
Name
Order
Citations
PageRank
Ying-Ming Wang13256166.96
Jian-Bo Yang23832203.05
Dong-Ling Xu3211994.13
Kwai-Sang Chin4103354.69