Burst the Filter Bubble: Using Semantic Web to Enable Serendipity

  • Valentina Maccatrozzo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7650)

Abstract

Personalization techniques aim at helping people dealing with the ever growing amount of information by filtering it according to their interests. However, to avoid the information overload, such techniques often create an over-personalization effect, i.e. users are exposed only to the content systems assume they would like. To break this “personalization bubble” we introduce the notion of serendipity as a performance measure for recommendation algorithms. For this, we first identify aspects from the user perspective, which can determine level and type of serendipity desired by users. Then, we propose a user model that can facilitate such user requirements, and enables serendipitous recommendations. The use case for this work focuses on TV recommender systems, however the ultimate goal is to explore the transferability of this method to different domains. This paper covers the work done in the first eight months of research and describes the plan for the entire PhD trajectory.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbassi, Z., Amer-Yahia, S., Lakshmanan, L.V.S., Vassilvitskii, S., Yu, C.: Getting Recommender Systems to Think Outside the Box. In: RecSys 2009, pp. 285–288 (2009)Google Scholar
  2. 2.
    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
  3. 3.
    Albanese, M., d’Acierno, A., Moscato, V., Persia, F., Picariello, A.: A Multimedia Semantic Recommender System for Cultural Heritage Applications. In: ICSC 2011, pp. 403–410 (2011)Google Scholar
  4. 4.
    André, P., schraefel, mc., Dumais Teevan, S.T.: Discovery Is Never by Chance: Designing for (Un)Serendipity. In: C & C 2009, pp. 305–314 (2009)Google Scholar
  5. 5.
    Aroyo, L., Stash, N., Wang, Y., Gorgels, P., Rutledge, L.: CHIP Demonstrator: Semantics-Driven Recommendations and Museum Tour Generation. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 879–886. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Brickley, D., Miller, L.: FOAF Vocabulary Specification 0.97. Namespace document, W3C (January 2010)Google Scholar
  7. 7.
    Chaomei, C.: Turning Points. The Nature of Creative Thinking. Springer (2011)Google Scholar
  8. 8.
    Campbell, D.T.: Blind Variation and Selective Retention in Creative Thought as in Other Knowledge Processes. Psychological Review 67, 380–400 (1960)CrossRefGoogle Scholar
  9. 9.
    Garcia, P.: Discovery by Serendipity: a new context for an old riddle. Foundations of Chemistry 11, 33–42 (2009)CrossRefGoogle Scholar
  10. 10.
    Gentner, D.: The mechanisms of analogical learning. In: Vosniadou, S., Ortony, A. (eds.) Similarity and Analogical Reasoning, pp. 199–241. Cambridge University Press (1989)Google Scholar
  11. 11.
    Ghosh, R., Dekhil, M.: Mashups for semantic user profiles. In: WWW 2008, pp. 1229–1230 (2008)Google Scholar
  12. 12.
    Golbeck, J., Hendler, J.: FilmTrust: movie recommendations using trust in web-based social networks. In: CCNC 2006, pp. 282–286 (2006)Google Scholar
  13. 13.
    Guilford, J.P.: The Nature of Human Intelligence. McGraw-Hill, New York (1967)Google Scholar
  14. 14.
    Hildebrand, M., van Ossenbruggen, J.R., Hardman, H.L., Wielemaker, J., Schreiber, G.: Searching In Semantically Rich Linked Data: A Case Study In Cultural Heritage. Technical Report INS-1001, CWI (2010)Google Scholar
  15. 15.
    Jiang, X., Tan, A.: Learning and inferencing in user ontology for personalized Semantic Web search. Information Sciences 179(16), 2794–2808 (2009)MATHCrossRefGoogle Scholar
  16. 16.
    Oku, K., Hattori, F.: Fusion-based Recommender System for Improving Serendipity. In: DiveRS 2011, pp. 19–26 (2011)Google Scholar
  17. 17.
    Oufaida, H., Nouali, O.: Exploiting Semantic Web Technologies for Recommender Systems: A Multi View Recommendation Engine. In: ITWP 2009 (2009)Google Scholar
  18. 18.
    Pariser, E.: The Filter Bubble. What the Internet is hiding from you. Penguin Press HC (2011)Google Scholar
  19. 19.
    Presutti, V., Aroyo, L., Adamou, A., Schopman, B., Gangemi, A., Schreiber, G.: Extracting Core Knowledge from Linked Data. In: COLD 2011 (2011)Google Scholar
  20. 20.
    Quan-Haase, A., Martin, K.: Digital Humanities: the continuing role of serendipity in historical research. In: iConference 2012, pp. 456–458 (2012)Google Scholar
  21. 21.
    van Aart, C., Aroyo, L., Brickley, D., Buser, V., Miller, L., Minno, M., Mostarda, M., Palmisano, D., Raimond, Y., Schreiber, G., Siebes, R.: The NoTube Beancounter: Aggregating User Data for Television Programme Recommendation. In: SDoW 2009 (2009)Google Scholar
  22. 22.
    van Andel, P.: Anatomy of the Unsought Finding. Serendipity: Origin, History, Domains, Traditions, Appearances, Patterns and Programmability. The British Journal for the Philosophy of Science 45(2), 631–648 (1994)CrossRefGoogle Scholar
  23. 23.
    van Erp, M., Oomen, J., Segers, R., van de Akker, C., Aroyo, L., Jacobs, G., Legêne, S., van der Meij, L., van Ossenbruggen, J.R., Schreiber, G.: Automatic Heritage Metadata Enrichment With Historic Events. In: MW 2011 (2011)Google Scholar
  24. 24.
    Walpole, H.: To Mann, Monday 18 January 1754. In: Lewis, W.S. (ed.) Horace Walpole’s Correspondence, vol. 20, pp. 407–411. Yale University Press (1960)Google Scholar
  25. 25.
    Zhang, Y.C., Séaghdha, D., Quercia, D., Jambor, T.: Auralist: Introducing Serendipity into Music Recommendation. In: WSDM 2012, pp. 13–22 (2012)Google Scholar
  26. 26.
    Ziegler, C.-N.: Semantic Web Recommender Systems. In: Lindner, W., Fischer, F., Türker, C., Tzitzikas, Y., Vakali, A.I. (eds.) EDBT 2004. LNCS, vol. 3268, pp. 78–89. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Valentina Maccatrozzo
    • 1
  1. 1.The Network Institute, Department of Computer ScienceVU UniversityAmsterdamThe Netherlands

Personalised recommendations