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Exploring Artificial Intelligence-based Data Fusion for Conjoint Analysis by Sungbin Cho; Seung Baek; Jonathan S. Kim is a Computer Science article available to read on EtoBox.

What is Exploring Artificial Intelligence-based Data Fusion for Conjoint Analysis about?

Conjoint analysis is used to understand how consumers develop preferences for products or services, which encompass, as usual, multiattributes and multi-attribute levels. Conjoint analysis has been one of the popular tools for multi-attribute decision-making problems on products and services for consumers over the last 30 years. It has also been used to market segmentation and optimal product positioning. In spite of its popularity and commercial success, a major weakness of conjoint analysis has been pointed such that respondents participating in conjoint experiment have to evaluate a number of hypothetical product profiles. To reduce the number of hypothetical products, this paper proposes a systematic method, called data fusion, and explores the usability of various data fusion techniques. The paper evaluates traditional data fusion (correlation-based), hierarchical Bayesian-based data fusion, and neural network-based data fusion.

Who reads Exploring Artificial Intelligence-based Data Fusion for Conjoint Analysis?

Author

Sungbin Cho; Seung Baek; Jonathan S. Kim

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0957-4174)

Published

2003

Language

EN

Field

Computer Science (Physical Sciences)