Many questions are posted on community websites in the world. Some of these questions are actually asked in order to receive empathy for the feelings of questioners, instead of getting specific answers to the questions asked. However, it is difficult to receive answers for these questions compared with questions that are asked for seeking responses other than for empathy. If such questions that are asked for the purpose of receiving empathy can get responses, it serves as an important factor to increase satisfaction of users. This paper reports on our attempt to improve response rate to the questions by classifying those questions that are asked for seeking empathy and those that are not by using machine learning and showing the questions classified as the ones seeking empathy to the prospective respondents who have been answered to these questions with higher rate.
This paper reports the result of analyzing sensor logs that are obtained from sensors installed in an actual nursing-care home and those attached to care staffs. Japan is now facing a super-aging society, and the number of elderly people needing care is increasing. ICT (information and communication technologies) is expected to play a key role to provide various care services while keeping privacy and QOL of elderly people. This paper aims to identify care staff’s activities as well as analyzes residents’ sleeping condition by using such sensors as commercially available with relatively low cost, such as a beacon, an acceleration sensor, and a sleep meter. The results of analyzing sensor logs obtained from an actual nursing-care home show that such activities as recreation and cleaning can be identified with relatively high accuracy. The analysis result also reveals typical sleep state of residents, and relationship between activities in daytime and sleep length.
In this paper, hybrid recommender system based on personal values-based collaborative filtering is proposed. Though collaborative filtering is one of the well-known technologies in recommender systems, it is known that their accuracy falls by biased ratings in datasets. This problem is expected to be solved by collaborative filtering based on personal values-based user model. However, user and item coverages might fall because its effect depends on users’ characteristics. Hybrid recommendation method combining existing and personal values-based collaborative filtering is therefore proposed to solve these problems. The experimental results show accuracy and coverages are improved by hybrid recommendation.
In this paper, we proposed advanced analytical method so that various analysis is possible. In the past, various analysis methods for human relationships have been proposed. Moreno proposed sociometry analysis and Yamashita et al. proposed an analysis method applying fuzzy graphs to it. Yamashita’s method is to analyze human relationships based on the partition tree. As preconditions for analysis, the nodes belonging to the same similarity in the partition tree are analyzed. In this paper, we provide a method that can analyze nodes belonging to any degree of similarity. Also, depending on the purpose of the analyst, a directed graph, such as a sociogram, may be more effective, or an undirected graph may be more effective. Therefore, there are both merits and demerits to using either method. We also aim to provide a new foundation that can analyze both of the two analysis methods.
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