Information recommendation system based on a probabilistic museum user model according to users' kansei

Abstract

User behavior modeling becomes important to create user-friendly products and services. To understand user behavior, we have to know the users' thinking, feelings, and "kansei." In our previous studies, we took a questionnaire to car purchasing users and made probabilistic user models about car selection. In these analyses, we showed that the selection of cars depends not only on the life-stages of customers but also on their kansei. In this study, to expand utilization of user modeling, we make an information recommendation system based on a kansei user model. This system recommends information about premium beer according to a respondent's philosophy concerning beer drinking and attitude to life, and then it measures the satisfaction level with regard to the recommendation. We tested this system at a museum having a history of beer exhibition and offered new beer drinking space. We showed that most users were satisfied with the recommendation results. We also showed that a number of museum users seemed to have little interest in beer and beer drinking.