title: Information filtering on coupled social networks.
abstract: 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.
authors.affiliation: Web Sciences Center, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Da-Cheng
authors.initials: DC
authors.lastname: Nie
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, People's Republic of China; Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, People's Republic of China; Alibaba Research Institute, Hangzhou, People's Republic of China.
authors.forename: Zi-Ke
authors.initials: ZK
authors.lastname: Zhang
authors.affiliation: Web Sciences Center, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Jun-Lin
authors.initials: JL
authors.lastname: Zhou
authors.affiliation: Web Sciences Center, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Fu
authors.affiliation: College of Communication Engineering, Chongqing University, Chongqing, People's Republic of China.
authors.forename: Kui
authors.initials: K
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D006801:Humans; D008962:Models, Theoretical; D060756:Social Networking