Paper Info
Title
A heuristic approach to discovering user correlations from organized social stream data
Abstract
Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling, experience and knowledge with each other on Internet. The high accessibility of these web sites has allowed the information to be spread across the social media more quickly and widely, which leads to more and more populations being engaged into this so-called social stream environment. All these make the organization of user relationships become increasingly important and necessary. In this study, we try to discover the potential and dynamical user correlations using those organized social streams in accordance with users' current interests and needs, in order to assist the collaborative information seeking process. We develop a heuristic approach to build a Dynamically Socialized User Networking (DSUN) model, and define a set of measures (such as interest degree, and popularity degree) and concepts (such as complementary tie, weak tie, and strong tie), to discover and represent users' current profiling and dynamical correlations. The corresponding algorithms are developed respectively. Finally, the architecture of the functional modules is presented, and the experiment results are demonstrated and discussed based on an application of the proposed model.
Year
DOI
Venue
2017
10.1007/s11042-014-2153-5
Multimedia Tools Appl.
Keywords
Field
DocType
Social data, Stream metaphor, Social network analysis, User correlation discovery, Information seeking
World Wide Web,Heuristic,Social media,Profiling (computer programming),Computer science,Collaborative information seeking,Information seeking,Social network analysis,Popularity,The Internet
Journal
Volume
Issue
ISSN
76
9
1573-7721
Citations 
PageRank 
References 
8
0.49
36
Authors
2
Name
Order
Citations
PageRank
Xiaokang Zhou122525.50
Jin, Q.223333.40