Multi-dimensional attributes and measures for dynamical user profiling in social networking environments

In this study, we concentrate on analyzing and building the dynamical user profiling to describe users’ multi-dimensional features and properties, in order to assist the individualized information seeking and recommendation process in social networking environments. A set of user attributes are introduced and defined to describe the basic user profiling in accordance with the analysis of information behaviors, and several centrality based measures are proposed and developed to describe the users’ importance and contributions with regards to a group of users based on their social connections in the DSUN (Dynamically Socialized User Networking) model. The experimental results are discussed to demonstrate the feasibility and effectiveness of our proposed methods.

[1]  Naresh Kumar Agarwal,et al.  Information source and its relationship with the context of information seeking behavior , 2011, iConference.

[2]  Manlai You,et al.  Research on Website Usage Behavior through Information Search Perspective: A Comparison of Experiential and Goal-Directed Behaviors , 2013, HCI.

[3]  Hamid Harroud,et al.  Dynamic user profiling approach for services discovery in mobile environments , 2010, IWCMC.

[4]  Qun Jin,et al.  User Correlation Discovery and Dynamical Profiling Based on Social Streams , 2012, AMT.

[5]  Sean P. Goggins,et al.  Twitter zombie: architecture for capturing, socially transforming and analyzing the twittersphere , 2012, GROUP.

[6]  Fernando Diaz,et al.  Improving recency ranking using twitter data , 2013, TIST.

[7]  Milad Shokouhi,et al.  Behavioral dynamics on the web: Learning, modeling, and prediction , 2013, TOIS.

[8]  Aristides Gionis,et al.  Social Network Analysis and Mining for Business Applications , 2011, TIST.

[9]  Rui Wang,et al.  Towards social user profiling: unified and discriminative influence model for inferring home locations , 2012, KDD.

[10]  Jie Tang,et al.  A Combination Approach to Web User Profiling , 2010, TKDD.

[11]  Rui Li,et al.  Multiple Location Profiling for Users and Relationships from Social Network and Content , 2012, Proc. VLDB Endow..

[12]  Barry Smyth,et al.  CatStream: categorising tweets for user profiling and stream filtering , 2013, IUI '13.

[13]  Jie Tang,et al.  Learning to predict reciprocity and triadic closure in social networks , 2013, TKDD.

[14]  Chin-Wan Chung,et al.  When daily deal services meet Twitter: understanding Twitter as a daily deal marketing platform , 2012, WebSci '12.

[15]  Bernard J. Jansen,et al.  A model for understanding collaborative information behavior in context: A study of two healthcare teams , 2008, Inf. Process. Manag..

[16]  Srinivasan Parthasarathy,et al.  An event-based framework for characterizing the evolutionary behavior of interaction graphs , 2007, KDD '07.

[17]  Di Jiang,et al.  Limosa: a system for geographic user interest analysis in Twitter , 2013, EDBT '13.

[18]  Sei-Ching Joanna Sin.  Modeling individual-level information behavior: A person-in-environment (PIE) framework , 2010, ASIST.

[19]  Qun Jin,et al.  Enriching user search experience by mining social streams with heuristic stones and associative ripples , 2012, Multimedia Tools and Applications.

[20]  Maria Fasli,et al.  Interest Aware Recommendations Based on Adaptive User Profiling , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[21]  Hongxia Jin,et al.  User activity profiling with multi-layer analysis , 2012, CIKM.

[22]  Alan Keller Gomes,et al.  Measuring media-based social interactions provided by smartphone applications in social networks , 2011, SBNMA '11.

[23]  Kenneth Wai-Ting Leung,et al.  A framework for personalizing web search with concept-based user profiles , 2012, TOIT.

[24]  Jingguo Wang,et al.  Drivers of information security search behavior: An investigation of network attacks and vulnerability disclosures , 2010, TMIS.

[25]  Stuart E. Middleton,et al.  Ontological user profiling in recommender systems , 2004, TOIS.

[26]  Dengguo Feng,et al.  Bind your phone number with caution: automated user profiling through address book matching on smartphone , 2013, ASIA CCS '13.

[27]  Huan Liu,et al.  Integrating Social Media Data for Community Detection , 2011, MSM/MUSE.

[28]  Stuart M. Allen,et al.  Cooperation through self-similar social networks , 2010, TAAS.

[29]  Wanda Pratt,et al.  Descriptive analysis of physical activity conversations on Twitter , 2011, CHI Extended Abstracts.

[30]  Rami Puzis,et al.  Computationally efficient link prediction in a variety of social networks , 2013, ACM Trans. Intell. Syst. Technol..

[31]  Ee-Peng Lim,et al.  Who is Retweeting the Tweeters? Modeling, Originating, and Promoting Behaviors in the Twitter Network , 2012, TMIS.