FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

• Published in: Concurrency and computation Vol. 30; no. 15
• Main Authors: Xue, Zhengyuan, Li, Ruixuan, Li, Yuhua, Huo, Lin, Gu, Xiwu, Xiao, Weijun
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.08.2018
• Subjects: Algorithms; followee; followee recommendation; follower; following behaviors; Network topologies; social network; Social networks
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.4514

Summary As social networks such as micro‐blogging sites rapidly grow, deciding whom to follow (followee recommendation) becomes a significantly important problem. Most existing works exclusively rely on two traditional factors: the proximity between two users in the network topology or the similarity of the user‐generated contents in the social network, disregarding the effect of users' following behaviors. The challenge of how to effectively combine these two factors remains largely open. Moreover, most research studies simply sort the scores to find top‐k users, which is time‐consuming, especially for large‐scale networks. In this paper, we propose the idea that “predict users' following behaviors by following behaviors themselves.” We consider a user's following to others as a normal process of dynamic and coherent behavior, and we model the potential propagation of the users' following behaviors. Furthermore, based on our previous research on top‐k selection problem, we propose an effective top‐k followee recommendation algorithm, called FRFB. FRFB has low complexity and high scalability and, moreover, good adaptability to real‐life dynamic social networks. We conduct extensive experiments, with two real social network data sets (Wiki and Twitter), which show that FRFB outperforms the well‐known topology‐based followee recommendation algorithms.