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Friend recommendation service is a common and important demand for the users on various online platforms. Current studies mainly focus on making predictions with the neighborhood and path information derived from the personal relationship networks. However, the formed links do not indicate that two users are familiar with each other nor have intimate connections. Selective treatments are made according...
Mobile Social Networks (MSNs) provide a variety of social networking applications in mobile environment, where social group finds and recruits potential members easily. Unfortunately, users enjoy these conveniences at the cost of revealing their personal data. Additionally, people usually ignore a critical factor, Impact Factor (IF), which is used to quantify group members' influence on their groups,...
Friend discovery has been one of the hot topics in our social activities over the past decade. Mobile users have more opportunities to discover and make new social interactions with others in vicinity to build and extend their social communities. However, the inevitable information releasing conflicts with the increasing privacy concerns. In this paper, we employ the concept of friend-of-friend and...
Privacy issues in group matching problem have become one of the most important things in Mobile Social Networks (MSNs) currently. Mobile users may feel uncomfortable when releasing personal information to some irrelevant people or groups. In this paper, we propose a practical group matching scheme without employing any Trusted Third Party (TTP) for privacy-aware users in MSNs. We first propose a fuzzy...
Making new social interactions with other users in vicinity is a crucial service in Proximity-based Mobile Social Networks (PMSNs), where a user can find a best matching friend directly through the Bluetooth/WiFi interfaces built in her mobile device. In existing work for such services, users have to publish their interests to do the matching. However, it conflicts with users' growing privacy concerns...
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