User Correlation Discovery and Dynamical Profiling Based on Social Streams

In this study, we try to discover the potential and dynamical user correlations using those reorganized social streams in accordance with users' current interests and needs, in order to assist the information seeking process. We develop a mechanism to build a Dynamical 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), which can discover and represent users' current profiling and dynamical correlations. The corresponding algorithms are developed respectively. Based on these, we finally discuss an application scenario of the DSUN model with experiment results.

[1]  Dawid Weiss,et al.  A survey of Web clustering engines , 2009, CSUR.

[2]  Kirsten A. Johnson.  The effect of Twitter posts on students’ perceptions of instructor credibility , 2011 .

[3]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.

[4]  Rynson W. H. Lau,et al.  Advances in Web-Based Learning - ICWL 2011 - 10th International Conference, Hong Kong, China, December 8-10, 2011. Proceedings , 2011, International Conference on Advances in Web-Based Learning.

[5]  Ryen W. White,et al.  Mining the search trails of surfing crowds: identifying relevant websites from user activity , 2008, WWW.

[6]  Reynol Junco,et al.  The effect of Twitter on college student engagement and grades , 2011, J. Comput. Assist. Learn..

[7]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[8]  Ed H. Chi,et al.  Information Seeking Can Be Social , 2009, Computer.

[9]  Qun Jin,et al.  Dynamical User Networking and Profiling Based on Activity Streams for Enhanced Social Learning , 2011, ICWL.

[10]  Olivia R. Liu Sheng,et al.  LinkSelector: A Web mining approach to hyperlink selection for Web portals , 2004, TOIT.

[11]  Hong Chen,et al.  Generating associative ripples of relevant information from a variety of data streams by throwing a heuristic stone , 2011, ICUIMC '11.

[12]  Andreas Hotho,et al.  Semantic Web Mining: State of the art and future directions , 2006, J. Web Semant..

[13]  Hong Chen,et al.  A Framework of Organic Streams: Integrating Dynamically Diversified Contents into Ubiquitous Personal Study , 2010, 2010 7th International Conference on Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing.

[14]  Ricardo Baeza-Yates,et al.  Query-sets: using implicit feedback and query patterns to organize web documents , 2008, WWW.