Consumer preference prediction by using a hybrid evidential reasoning and belief rule-based methodology

Authors:
Ying-Ming Wang;Jian-Bo Yang;Dong-Ling Xu;Kwai-Sang Chin
Affiliations:
School of Public Administration, Fuzhou University, Fuzhou 350002, PR China;Manchester Business School, The University of Manchester, Manchester M15 6PB, UK and Management School, Hefei University of Technology, Anhui 230009, PR China;Manchester Business School, The University of Manchester, Manchester M15 6PB, UK and Management School, Hefei University of Technology, Anhui 230009, PR China;Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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.