Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carpineto, C., Osiński, S., Romano, G., Weiss, D.: A Survey of Web Clustering Engines. ACM Computing Surveys (CSUR) 41(3) (2009)
Chi, E.H.: Information Seeking Can Be Social. Computer 42(3), 42–46 (2009)
Chen, H., Zhou, X.K., Man, H.F., Wu, Y., Ahmed, A.U., Jin, Q.: A Framework of Organic Streams: Integrating Dynamically Diversified Contents into Ubiquitous Personal Study. In: 2nd International Symposium on Multidisciplinary Emerging Networks and Systems. Xi’an (2010)
Zhou, X.K., Chen, H., Jin, Q., Yong, J.M.: Generating Associative Ripples of Relevant Information from a Variety of Data Streams by Throwing a Heuristic Stone. In: ACM ICUIMC 2011 (5th International Conference on Ubiquitous Information Management and Communication), Seoul, Korea (2011)
Zhou, X., Jin, Q.: Dynamical User Networking and Profiling Based on Activity Streams for Enhanced Social Learning. In: Leung, H., Popescu, E., Cao, Y., Lau, R.W.H., Nejdl, W. (eds.) ICWL 2011. LNCS, vol. 7048, pp. 219–225. Springer, Heidelberg (2011)
Signorini, A., Segre, A.M., Polgreen, P.M.: The Use of Twitter to Track Levels of Disease Activity and Public Concern in the US during the Influenza A H1N1 Pandemic. PLOS ONEÂ 6(5) (2011)
Junco, R., Heiberger, G., Loken, E.: The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning 27(2), 119–132 (2011)
Johnson, K.A.: The effect of Twitter posts on students’ perceptions of instructor credibility. Learning Media and Technology 36(1), 21–38 (2011)
Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. ACM SIGKDD 1(2), 12–23 (2000)
Stumme, G., Hotho, A., Berendt, B.: Semantic Web Mining State of the Art and Future Directions. Elsevier Web Semantics: Science, Services and Agents on the World Wide Web 4(2), 124–143 (2006)
Poblete, B., Baeza-Yates, R.: Query-Sets: Using Implicit Feedback and Query Patterns to Organize Web Documents. In: Proc. WWW 2008, Beijing, pp. 41–48 (2008)
Bilenko, M., White, R.W.: Mining the Search Trails of Surfing Crowds: Identifying Relevant Websites From User Activity. In: Proc. WWW 2008, Beijing, pp. 51–60 (2008)
Fang, X., Liu Sheng, O.R.: LinkSelector: A Web Mining Approach to Hyperlink Selection for Web Portals. ACM Transactions on Internet Technology 4(2), 209–237 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, X., Jin, Q. (2012). User Correlation Discovery and Dynamical Profiling Based on Social Streams. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-35236-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35235-5
Online ISBN: 978-3-642-35236-2
eBook Packages: Computer ScienceComputer Science (R0)