title: Information filtering via biased random walk on coupled social network.
abstract: The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods.
authors.affiliation: Web Sciences Center, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Da-Cheng
authors.identifier: 0000-0003-0965-2331
authors.initials: DC
authors.lastname: Nie
authors.affiliation: Institute of Information Economy, Hangzhou Normal University, Hangzhou 311121, China ; Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, China.
authors.forename: Zi-Ke
authors.identifier: 0000-0002-6203-7621
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 610054, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Dong
authors.affiliation: Web Sciences Center, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Chongjing
authors.identifier: 0000-0002-4989-6798
authors.initials: C
authors.lastname: Sun
authors.affiliation: Web Sciences Center, School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Fu
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical