Information filtering on coupled social networks

• Published in: PloS one Vol. 9; no. 7; p. e101675
• Main Authors: Nie, Da-Cheng, Zhang, Zi-Ke, Zhou, Jun-Lin, Fu, Yan, Zhang, Kui
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.07.2014; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Collaboration; Computer and Information Sciences; Computer science; Data mining; Discriminant analysis; Electronic commerce; Empirical analysis; Engineering; Filtration; Humans; International conferences; Internet; Models, Theoretical; On-line systems; Physical Sciences; Recommender systems; Social behavior; Social interactions; Social Networking; Social networks; Social organization; Structure-function relationships; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0101675

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.