ATFR: Attribute based Trustworthy Friend Recommendations Online

2020 ◽  
Author(s):  
Manasa S M ◽  
Manasa R ◽  
Venugopal K R
Keyword(s):  
Friend Recommendations

scholarly journals Exploring Types of Information Sources Used When Choosing Doctors: Observational Study in an Online Health Care Community (Preprint)

2020 ◽  
Author(s):  
Shuang Zhang ◽  
Jying-Nan Wang ◽  
Ya-Ling Chiu ◽  
Yuan-Teng Hsu
Keyword(s):  
Health Care ◽  
Medical Decision Making ◽  
Information Sources ◽  
Information Source ◽  
Online Reviews ◽  
Medical Decision ◽  
Medical Specialties ◽  
Care Community ◽  
Types Of Information ◽  
Friend Recommendations

BACKGROUND Patients attempt to make appropriate decisions based on their own knowledge when choosing a doctor. In this process, the first question usually faced is that of how to obtain useful and relevant information. This study investigated the types of information sources that are used widely by patients in choosing a doctor and identified ways in which the preferred sources differ in various situations. OBJECTIVE This study aims to address the following questions: (1) What is the proportion in which each of the various information sources is used? (2) How does the information source preferred by patients in choosing a doctor change when there is a difference in the difficulty of medical decision making, in the level of the hospital, or in a rural versus urban situation? (3) How do information sources used by patients differ when they choose doctors with different specialties? METHODS This study overcomes a major limitation in the use of the survey technique by employing data from the Good Doctor website, which is now China's leading online health care community, data which are objective and can be obtained relatively easily and frequently. Multinomial logistic regression models were applied to examine whether the proportion of use of these information sources changes in different situations. We then used visual analysis to explore the question of which type of information source patients prefer to use when they seek medical assistance from doctors with different specialties. RESULTS The 3 main information sources were online reviews (OR), family and friend recommendations (FR), and doctor recommendations (DR), with proportions of use of 32.93% (559,345/1,698,666), 23.68% (402,322/1,698,666), and 17.48% (296,912/1,698,666), respectively. Difficulty in medical decision making, the hospital level, and rural-urban differences were significantly associated with patients’ preferred information sources for choosing doctors. Further, the sources of information that patients prefer to use were found to vary when they looked for doctors with different medical specialties. CONCLUSIONS Patients are less likely to use online reviews when medical decisions are more difficult or when the provider is not a tertiary hospital, the former situation leading to a greater use of online reviews and the latter to a greater use of family and friend recommendations. In addition, patients in large cities are more likely to use information from online reviews than family and friend recommendations. Among different medical specialties, for those in which personal privacy is a concern, online reviews are the most common source. For those related to children, patients are more likely to refer to family and friend recommendations, and for those related to surgery, they value doctor recommendations more highly. Our results can not only contribute to aiding government efforts to further promote the dissemination of health care information but may also help health care industry managers develop better marketing strategies.

scholarly journals Strategi Promosi Perekrutan Siswa Di SMK Bisnis Dan Teknologi Terhadap Minat Siswa Untuk Mendaftar

2019 ◽  
Vol 15 (1) ◽  
pp. 69-94
Keyword(s):  
Research Method ◽  
Vocational Schools ◽  
Sources Of Information ◽  
Teacher Recommendations ◽  
Parent Choices ◽  
Friend Recommendations

This research concerns students' interest in deciding to enroll in Business and Technology Vocational Schools in terms of factors that influence students from information obtained through promotional media used by Vocational Business and Technology namely Banners, Brochures, Presentations, Friend Recommendations, Parent Choices , Own Choices, Teacher Recommendations and Others. The purpose of this research is to determine what strategies must be done by the Vocational Business and Technology through the promotional media used. The results of the 60 respondents showed that from eight sources of information about the Business and Technology Vocational Schools obtained by these students, the Brochure ranked first, followed by Parent Choices and then Friend Recommendations. The research method used is a survey using a questionnaire. The data obtained is then analyzed and presented in the form of tabulations.

scholarly journals Hybrid Deep Neural Networks for Friend Recommendations in Edge Computing Environment

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 10693-10706
Author(s):  
Jibing Gong ◽  
Yi Zhao ◽  
Shuai Chen ◽  
Hongfei Wang ◽  
Linfeng Du ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Edge Computing ◽  
Computing Environment ◽  
Friend Recommendations

Exploiting User Check-In Data for Geo-Friend Recommendations in Location-Based Social Networks

2020 ◽  
Vol 11 (2) ◽  
pp. 1-17
Author(s):  
Shudong Liu ◽  
Ke Zhang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Time Slot ◽  
Empirical Studies ◽  
Real Life ◽  
Periodic Trajectories ◽  
The Public ◽  
Friend Recommendation ◽  
Location Based Social Networks ◽  
Friend Recommendations

The development of Web 2.0 technologies has meant that online social networks can both help the public facilitate sharing and communication and help them make new friends through their cyberspace social circles. Generating more accurate and geographically related results to help users find more friends in real life is gradually becoming a research hotspot. Recommending geographically related friends and alleviating check-in data sparsity problems in location-based social networks allows those to divide a day into different time slots and automatically collect user check-in data at each time slot over a certain period. Second, some important location points or regions are extracted from raw check-in trajectories, temporal periodic trajectories are constructed, and a geo-friend recommendation framework is proposed that can help users find geographically related friends. Finally, empirical studies from a real-world dataset demonstrate that this paper's method outperforms other existing methods for geo-friend recommendations in location-based social networks.

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