Interest prediction in social networks based on Markov chain modeling on clustered users. (14th July 2015)

• Record Type: Journal Article
• Authors: Zheng, Xianghan; An, Dongyun; Chen, Xing; Guo, Wenzhong
• Other Names: Higuera‐Toledano M. Teresa guestEditor.; Brinkschulte Uwe guestEditor.; Rettberg Achim guestEditor.; Qiang Weizhong guestEditor.; Zheng Xianghan guestEditor.; Hsu Ching‐Hsien guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 14(2016)
• Page Start: 3895
• Page End: 3909
• Publication Date: 2015-07-14
• Subjects: social network -- single‐Markov chain -- multi‐Markov chain -- interest eigenvalues -- clustering; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3572
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Effective user interest prediction is significant for service providers in a set of application scenarios such as user behavior analysis and resource recommendation. However, existing approaches are either incomplete or proprietary. In this paper, user interest prediction based on the Markov chain modeling on clustered users is proposed with the following procedure: collect dataset from 4613 users and more than 16 million messages from Sina Weibo; obtain each user's interest eigenvalue sequence and establish single‐Markov chain model; and implement user clustering algorithm for the multi‐Markov chain construction in order to divide users into a set of predefined interest categories. The proposed solution is capable of predicting both long‐term and short‐term user interests based on a suitable selection of the initial state distribution, λ . The proposed solution also proves that short‐term interests are consistent with long‐term interests if the influences of social or user‐related events that cause interruptions (e.g., earthquake and birthday) are not considered. Furthermore, experiments show that the proposed solution is feasible and efficient and can achieve a higher accuracy of prediction than that of the other approaches such as Support Vector Machine (SVM) and K‐means. Copyright © 2015 John Wiley & Sons, Ltd.