Fereshteh-Azadi Parand; Farzam Matinfar; Fatemeh Mehdikhanloo
Abstract
Mobile social networks facilitate connections through mobile devices, and users of these networks can use mobile to access, share and distribute information. With increasing the number of users on social networks, the large amount of shared information and the dissemination of created information cause ...
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Mobile social networks facilitate connections through mobile devices, and users of these networks can use mobile to access, share and distribute information. With increasing the number of users on social networks, the large amount of shared information and the dissemination of created information cause some problems such as rumor propagation and access to incorrect information. The most powerful tools for validity of received information is based upon the trust value which is assigned by the others. However, considering the subjective and perceptive nature of the concept of trust, the mapping of trust in a computational model is one of the important issues in computing systems of social networks. In addition, there may be various communities on social networks and all users will not be directly connected to each other, which leads to a more complex process of calculating trust. In this research, using user characteristics in social networks, a fuzzy classification approach is proposed, and the trust is computed between users in a class using a computational model. Also trust is gained between users who are not directly connected, using the combination, transition and aggregation processes. By comparing the results, it can be seen that the proposed method recognizes trustworthy people with high precision.
seyed saeed mortazavi; Fereshteh-Azadi Parand
Abstract
Health and health services are two inseparable parts of one's life. Each person has had different needs for health services at least several times during their life cycle and would resolve them with available facilities. Regarding the high popularity of social networks in the last two decades, one of ...
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Health and health services are two inseparable parts of one's life. Each person has had different needs for health services at least several times during their life cycle and would resolve them with available facilities. Regarding the high popularity of social networks in the last two decades, one of the tools that can provide many opportunities for people in the health field is social networking. In this research, we introduce a health social network which focuses on users or patients’ association with doctors and a variety of health services. In order to improve this network’s performance, we suggest a recommender system that can offer users a doctor, a special expertise in order to ask medical consultation, or an article, based on their needs. We have used heterogeneous information networks for modeling the health social network. These networks cover several types of objects, such as physicians, patients and consultation, and also several types of relationships, such as requesting or answering a consultation. For the recommender model, we use each user’s implicit feedback which they register on the network, according to the methods provided by the heterogeneous information networks. Bayesian Personalized Ranking is used in recommender model’s learning algorithm. This algorithm is a combination of ranking scores method and the foresaid learning algorithm. In the end, we will show how to use this social network and the recommender system, by applying the suggested method on our dataset.
