Interweaving Public User Profiles on the Web

  • Fabian Abel
  • Nicola Henze
  • Eelco Herder
  • Daniel Krause
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)


While browsing the Web, providing profile information in social networking services, or tagging pictures, users leave a plethora of traces. In this paper, we analyze the nature of these traces. We investigate how user data is distributed across different Web systems, and examine ways to aggregate user profile information. Our analyses focus on both explicitly provided profile information (name, homepage, etc.) and activity data (tags assigned to bookmarks or images). The experiments reveal significant benefits of interweaving profile information: more complete profiles, advanced FOAF/vCard profile generation, disclosure of new facets about users, higher level of self-information induced by the profiles, and higher precision for predicting tag-based profiles to solve the cold start problem.


Individual Service Social Networking Service Cold Start Problem Public User Social Media Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Jameson, A.: Adaptive interfaces and agents. In: The HCI handbook: fundamentals, evolving technologies and emerging applications, pp. 305–330 (2003)Google Scholar
  2. 2.
    Zang, N., Rosson, M.B., Nasser, V.: Mashups: Who? What? Why? In: Czerwinski, M., Lund, A., Tan, D. (eds.) Proceedings of Conference on Human factors in computing systems (CHI ’08), pp. 3171–3176. ACM, New York (2008)Google Scholar
  3. 3.
    Brickley, D., Miller, L.: FOAF Vocabulary Specification 0.91. Namespace document, FOAF Project (November 2007),
  4. 4.
    Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: gumo – the general user model ontology. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 428–432. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Aroyo, L., Dolog, P., Houben, G., Kravcik, M., Naeve, A., Nilsson, M., Wild, F.: Interoperability in pesonalized adaptive learning. Journal of Educational Technology & Society 9 (2), 4–18 (2006)Google Scholar
  6. 6.
    Carmagnola, F., Cena, F.: User identification for cross-system personalisation. Information Sciences: an International Journal 179(1-2), 16–32 (2009)Google Scholar
  7. 7.
    Yudelson, M., Brusilovsky, P., Zadorozhny, V.: A user modeling server for contemporary adaptive hypermedia: An evaluation of the push approach to evidence propagation. In: Conati, C., McCoy, K.F., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 27–36. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Assad, M., Carmichael, D., Kay, J., Kummerfeld, B.: PersonisAD: Distributed, active, scrutable model framework for context-aware services. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 55–72. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Abel, F., Heckmann, D., Herder, E., Hidders, J., Houben, G.J., Krause, D., Leonardi, E., van der Slujis, K.: A framework for flexible user profile mashups. In: Dattolo, A., Tasso, C., Farzan, R., Kleanthous, S., Vallejo, D.B., Vassileva, J. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 1–10. Springer, Heidelberg (2009)Google Scholar
  10. 10.
    Szomszor, M., Alani, H., Cantador, I., O’Hara, K., Shadbolt, N.: Semantic modelling of user interests based on cross-folksonomy analysis. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T.W., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 632–648. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Stewart, A., Diaz-Aviles, E., Nejdl, W., Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L.: Cross-tagging for personalized open social networking. In: Cattuto, C., Ruffo, G., Menczer, F. (eds.) Hypertext, pp. 271–278. ACM, New York (2009)Google Scholar
  12. 12.
    Firan, C.S., Nejdl, W., Paiu, R.: The benefit of using tag-based profiles. In: Almeida, V.A.F., Baeza-Yates, R.A. (eds.) LA-WEB, pp. 32–41. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  13. 13.
    Michlmayr, E., Cayzer, S.: Learning User Profiles from Tagging Data and Leveraging them for Personal(ized) Information Access. In: Golder, S., Smadja, F. (eds.) Proceedings of the Workshop on Tagging and Metadata for Social Information Organization at WWW ’07 (May 2007)Google Scholar
  14. 14.
    Dawson, F., Howes, T.: vCard MIME Directory Profile. Request for comments, IETF, Network Working Group (September 1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fabian Abel
    • 1
  • Nicola Henze
    • 1
  • Eelco Herder
    • 1
  • Daniel Krause
    • 1
  1. 1.IVS – Semantic Web Group & L3S Research CenterLeibniz University HannoverGermany

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