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GeniUS: Generic User Modeling Library for the Social Semantic Web

  • Qi Gao
  • Fabian Abel
  • Geert-Jan Houben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7185)

Abstract

In this paper, we present GeniUS, a generic topic and user modeling library for the Social Semantic Web that enriches the semantics of social data and status messages particularly. Given a stream of messages, it allows for generating topic and user profiles that summarize the stream according to domain- and application-specific needs which can be specified by the requesting party. Therefore, GeniUS can be applied in various application settings. In this paper, we analyze and evaluate GeniUS in six different application domains. Given users’ status messages from Twitter, we investigate the quality of profiles that are generated by different GeniUS user modeling strategies for supporting various recommendation tasks ranging from product recommendations to more specific recommendations as required in book or software product stores. Our evaluation shows that GeniUS succeeds in inferring the semantic meaning of Twitter status messages. We prove that it can successfully adapt to a given domain and application context allowing for tremendous improvements of the recommendation quality when domain-specific semantic filtering is applied to remove noise from the profiles.

Keywords

user modeling social web semantic web twitter semantic enrichment filtering 

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References

  1. 1.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM, Raleigh (2010)CrossRefGoogle Scholar
  2. 2.
    Chen, J., Nairn, R., Chi, E.H.: Speak Little and Well: Recommending Conversations in Online Social Streams. In: Proceedings of the 29th International Conference on Human Factors in Computing Systems, CHI 2011, pp. 217–226. ACM, Vancouver (2011)Google Scholar
  3. 3.
    Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing User Modeling on Twitter for Personalized News Recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Abel, F., Herder, E., Houben, G.J., Henze, N., Krause, D.: Cross-system User Modeling and Personalization on the Social Web. User Modeling and User-Adapted Interaction (UMUAI), Special Issue on Personalization in Social Web Systems (to appear), http://wis.ewi.tudelft.nl/papers/2011-umuai-cross-system-um.pdf
  5. 5.
    Kobsa, A.: Generic user modeling systems. User Modeling and User-Adapted Interaction 11(1-2), 49–63 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Berkovsky, S., Kuflik, T., Ricci, F.: Cross-Domain Mediation in Collaborative Filtering. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 355–359. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Mehta, B., Niederee, C., Stewart, A.: Towards cross-system personalization. In: International Conference on Universal Access in Human-Computer Interaction, Las Vegas, Nevada, USA (UAHCI 2005). Lawrence Erlbaum Associates (2005)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Brickley, D., Miller, L.: FOAF Vocabulary Specification 0.91. Namespace document, FOAF Project (November 2007), http://xmlns.com/foaf/0.1/
  10. 10.
    Bojars, U., Breslin, J.G.: SIOC Core Ontology Specification. Namespace document, DERI, NUI Galway (January 2009), http://rdfs.org/sioc/spec/
  11. 11.
    Brickley, D., Miller, L., Inkster, T., Zeng, Y., Wang, Y., Damljanovic, D., Huang, Z., Kinsella, S., Breslin, J., Ferris, B.: The Weighted Interests Vocabulary 0.5. Namespace document, Sourceforge (September 2010)Google Scholar
  12. 12.
    Firan, C.S., Nejdl, W., Paiu, R.: The Benefit of Using Tag-based Profiles. In: Proceedings of the 2007 Latin American Web Conference (LA-WEB 2007), pp. 32–41. IEEE Computer Society, Washington, DC, USA (2007)CrossRefGoogle Scholar
  13. 13.
    Sen, S., Vig, J., Riedl, J.: Tagommenders: connecting users to items through tags. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 671–680. ACM, Madrid (2009)CrossRefGoogle Scholar
  14. 14.
    Cai, Y., Li, Q.: Personalized search by tag-based user profile and resource profile in collaborative tagging systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 969–978. ACM, Toronto (2010)Google Scholar
  15. 15.
    Laniado, D., Mika, P.: Making Sense of Twitter. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 470–485. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Rowe, M., Stankovic, M., Laublet, P.: Mapping Tweets to Conference Talks: A Goldmine for Semantics. In: Social Data on the Web Workshop at the 9th International Semantic Web Conference (ISWC), Shanghai, China, vol. 664 (2010), CEUR-WS.org
  17. 17.
    Abel, F., Gao, Q., Houben, G.J., Tao, K.: Semantic Enrichment of Twitter Posts for user Profile Construction on the Social Web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 201. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Mendes, P.N., Jakob, M., García-Silva, A., Bizer, C.: Dbpedia spotlight: Shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems (I-Semantics), Graz, Austria, pp. 1–8 (September 2011)Google Scholar
  19. 19.
    Gao, Q., Abel, F., Houben, G.J., Tao, K.: Interweaving trend and user modeling for personalized news recommendations. In: Proceeding of the 2011 Internation Conference on Web Intelligence Web (WI 2011), pp. 100–103. IEEE Press, Lyon (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qi Gao
    • 1
  • Fabian Abel
    • 1
  • Geert-Jan Houben
    • 1
  1. 1.Web Information SystemsDelft University of TechnologyThe Netherlands

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