A Model of Sensitivity to Get Tired - Toward a Cool Recommendation

抄録

With the evolution and spread of information technologies including Web, the amount of information that users can obtain has dramatically increased. Huge quantities of information have made it difficult for users to reach their desired information. In order to address this problem, various information recommendation systems including cooperative filtering have studied and developed with certain results. Many of traditional studies on information recommendation provide a method to statistically determine, based on the user's preferences and behavioral history, the right information to recommend. Because the user's request depends on their situation as well, there are also information recommendation studies that take the user context into consideration. These traditional studies, however, aim at recommending highly satisfactory information in a one-time or small number of uses of a system or service, and never take it into account that the user would feel tired of the same system or service after they have used it many times over. In reality, if the user can obtain information to their preference, they would feel very satisfied with it at first, however, if they are only presented with similar information every time, they may eventually get tired of the system and cease to use it. Consequently, in order to realize an information recommendation system that the user wants to continue to use, you need consideration of a human nature of being tired of the same thing, as well as interaction design for information recommendation that leverages its characteristics. This study has modeled the human nature of being tired of the same thing and enabled the measurement of the degree of such boredom. Also, the study has implemented an information recommendation system that has features to (1) visualize the degree of the user's boredom and (2) recommend information that can reduce it. Then, an evaluation experiment was conducted using a system developed. The result shows that an information recommendation system proposed in this study can get the user less bored and more satisfied when used continuously. In addition, it enables the user to make various choices even in a limited content. These results suggest that both information recommendation based on a Kansei model of human boredom and interaction that visualizes the degree of the user's boredom and makes them conscious of it work well for their continuous use of the system.