Profile Matching in Electronic Social Networks Using a Matching Measure for Fuzzy Numerical Attributes and Fields of Interests

DOI: 10.4236/am.2014.516250   PDF   HTML   XML   3,046 Downloads   3,626 Views  


The problem of profile matching in electronic social networks asks to find those offering profiles of actors in the network fitting best to a given search profile. In this article this problem is mathematically formulated as an optimization problem. For this purpose the underlying search space and the objective function are defined precisely. In particular, data structures of search and offering profiles are proposed, as well as a function measuring the matching of the attributes of a search profile with the corresponding attributes of an offering profile. This objective function, given in Equation (29), is composed of the partial matching degrees for numerical attributes, discrete non-numerical attributes, and fields of interests, respectively. For the matching degree of numerical profile attributes a fuzzy value approach is presented, see Equation (22), whereas for the matching degree of fields of interest a new measure function is introduced in Equation (26). The resulting algorithm is illustrated by a concrete example. It not only is applicable to electronic social networks but also could be adapted for resource discovery in grid computation or in matchmaking energy demand and supply in electrical power systems and smart grids, especially to efficiently integrate renewable energy resources.

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Vries, A. (2014) Profile Matching in Electronic Social Networks Using a Matching Measure for Fuzzy Numerical Attributes and Fields of Interests. Applied Mathematics, 5, 2619-2629. doi: 10.4236/am.2014.516250.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Augusto, L.R., de Castro, R.C.F., Franco, L.G., Machado, L.G.P. and Seo, C.E. (2007) Multiple Interest Matchmaking in Personal Business Networks. US Patent US7953673 B2.
[2] Gribova, V. and Kachanov, P. (2009) An Approach to Automated User Interest Matching in Online Classified Advertising Systems. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J. and Bevilacqua, V., Eds., Emerging Intelligent Computing Technology and Applications, Lecture Notes in Computer Science, Vol. 5754, Springer, Berlin, 665-673.
[3] Al Rabea, A.I. and Al Fraihat, M.M.A. (2012) A New Matchmaking Algorithm Based on Multi-Level Matching Mechanism Combined with Fuzzy Set. Journal of Software Engineering and Applications, 5, 110-118.
[4] Barnes, J.A. (1954) Class and Committees in a Norwegian Island Parish. Human Relations, 7, 39-58.
[5] Easley, D. and Kleinberg, J. (2010) Networks, Crowds, and Markets. Reasoning about a Highly Connected World. Cambridge University Press, Cambridge.
[6] Wasserman, S. and Faust, K. (1994) Social Network Analysis. Cambridge University Press, Cambridge.
[7] Newman, M.E.J. (2010) Networks. An Introduction. Oxford University Press, Oxford.
[8] Hill, R.A. and Dunbar, R.I.M. (2003) Social Network Size in Humans. Human Nature, 14, 53-72.
[9] O’Reilly, T. (2007) What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. Communications & Strategies, 1, 17.
[10] Agarwal, S. and Lamparter, S. (2005) sMart—A Semantic Matchmaking Portal for Electronic Markets. Proceedings of the 7th International IEEE Conference on E-Commerce Technology, Munich, IEEE Computer Society.
[11] Berghella, M., Calí, A., Capata, A., Catarci, T., Cerrocchi, P., Masi, P., Oppedisano, M., Trevisani, E. and Vitaletti, A. (2005) SmartDate: User Adaptation in Location-Based Mobile Matchmaking. PSMD 05: Proceedings of the 2005 International Workshop on Plastic Services for Mobile Devices, Rome, 12 September 2005.
[12] Cal, A., Calvanese, D., Colucci, S., Di Noia, T. and Donini, F.M. (2004) A Description Logic Based Approach for Matching User Profiles. Proceedings of the 17th International Workshop on Description Logics (DL’04), 104. CEUR Workshop Proceedings.
[13] de Vries, A. (2007) XML Framework for Concept Description and Knowledge Representation. Aachen.
[14] Bai, X., Yu, H., Ji, Y. and Marinescu, D.C. (2004) Resource Matching and a Matchmaking Service for an Intelligent Grid. International Journal of Computational Intelligence, 1, 197-205.
[15] Foster, I. and Kesselman, C. (2004) The Grid 2. Blueprint for a New Computing Infrastructure. Elsevier, Amsterdam.
[16] Prodan, R. and Fahringer, T. (2006) Grid Computing: Experiment Management, Tool Integration, and Scientific Workflows. Springer-Verlag, Heidelberg & Berlin.
[17] Raman, R., Livny, M. and Solomon, M.H. (1998) Matchmaking: Distributed Resource Management for High Throughput Computing. The 7th International Symposium on High Performance Distributed Computing, Chicago, 28-31 July 1998, 140-146.
[18] González-Castaáo, F.J., Vales-Alonso, J., Livny, M., Costa-Montenegro, E. and Anido-Rifón, L.E. (2003) Condor Grid Computing from Mobile Handheld Devices. Mobile Computing and Communications Review, 7, 117-126.
[19] Lodygensky, O., Fedak, G., Cappello, F., Néri, V., Livny, M. and Thain, D. (2003) XtremWeb & Condor Sharing Resources between Internet Connected Condor Pools. Proceedings of the 3rd International Symposium on Cluster Computing and the Grid, 382-389.
[20] Thain, D. and Livny, M. (2003) Building Reliable Clients and Services. Foster and Kesselman, 15, 285-318.
[21] Thain, D., Tannenbaum, T. and Livny, M. (2005) Distributed Computing in Practice: The Condor Experience. Concurrency, Practice and Experience, 17, 323-356.
[22] Edenhofer, O., Madruga, R.P., Sokona, Y., Seyboth, K., Matschoss, P., Kadner, S., Zwickel, T., Eickemeier, P., Hansen, G., Schlömer, S. and von Stechow, C. (2012) Renewable Energy Sources and Climate Change Mitigation. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

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