Impact factor-based group recommendation scheme with privacy preservation in MSNs
Yuanyuan He, Kuan Zhang, Hanyi Wang, Fenghua Li, Ben Niu, Hui Li · 2017
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, since a group member with larger IF generally has a greater influence on potential new member recommendation. In this paper, we propose IF-RG, an IF-based group recommendation scheme with privacy preservation in MSNs. First, we construct a transmission matrix and exploit PageRank algorithm to compute and update group members' IFs. The average variation of IF is formed to measure convergence speed of the iteration method of computing IF. To make sure that the larger IFs, the more influence, IF, Ochiai similarity function and weighted majority rule are integrated in the novel matching degree between stranger and group. The fuzzy matrix algorithm not only protects users' privacy, but also helps our scheme to support group recommendation when not every one in the groups is online. Finally, security and performance are analyzed and evaluated via detailed simulations.