scholarly journals Development of Mobile Social Network Systems Using Real-Time Facial Authentication and Collaborative Recommendations

2013 ◽  
Vol 9 (12) ◽  
pp. 820979
Author(s):  
Hyeong-Joon Kwon ◽  
Dong-Ju Kim ◽  
Kwang-Seok Hong
Informatics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 8
Author(s):  
Ira Puspitasari ◽  
Shukor Sanim Mohd Fauzi ◽  
Cheng-Yuan Ho

Participatory medicine and e-health help to promote health literacy among non-medical professionals. Users of e-health systems actively participate in a patient social network system (PSNS) to share health information and experiences with other users with similar health conditions. Users’ activities provide valuable healthcare resources to develop effective participatory medicine between patients, caregivers, and medical professionals. This study aims to investigate the factors of patients’ engagement in a PSNS by integrating and modifying an existing behavioral model and information system model (i.e., affective events theory (AET) and self-determination theory (SDT)). The AET is used to model the structure, the affective aspects of the driven behavior, and actual affective manifestation. The SDT is used to model interest and its relations with behavior. The data analysis and model testing are based on structural equation modeling, using responses from 428 users. The results indicate that interest and empathy promote users’ engagement in a PSNS. The findings from this study suggest recommendations to further promote users’ participation in a PSNS from the sociotechnical perspective, which include sensitizing and constructive engagement features. Furthermore, the data generated from a user’s participation in a PSNS could contribute to the study of clinical manifestations of disease, especially an emerging disease.


2013 ◽  
Vol 37 ◽  
pp. 15-30 ◽  
Author(s):  
Lei Jin ◽  
James B.D. Joshi ◽  
Mohd Anwar

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.


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