Department   Undergraduate School  , School of Commerce
   Position   Professor
Language English
Publication Date 2018/04
Type Academic Journal
Peer Review Peer reviewed
Title "Case-Based Decision Model Matches Ideal Point Model: Application to Marketing Decision Support System", Journal of Intelligent Information Systems
Contribution Type Co-authored (other than first author)
Journal Journal of Intelligent Information Systems
Journal TypeAnother Country
Publisher Springer
Volume, Issue, Page 50(2),pp.341-362
Author and coauthor Keita Kinjo and Takeshi Ebina
Details This paper studies the relationship between a case-based decision theory (CBDT) and an ideal point model (IPM). We show a case-based decision model (CBDM) can be transformed into an IPM under some assumptions. This transformation can allow us to visualize the relationship among data and simplify the calculations of distance between one current datum and the ideal point, rather than the distances between data. Our results will assist researchers with their product design analysis and positioning of goods through CBDT, by revealing past dependences or providing a reference point. Furthermore, we use data on the viewing behavior of audiences of TV dramas in Japan and compare the estimation results under the CBDM that corresponds to a standard decision model with similarities and other various similarity functions and without a similarity function. Our empirical analysis shows the CBDM with a similarity function, presented in this study, best fits the data.