Finding My Needle in the Haystack: Effective Personalized Re-ranking of Search Results in Prospector

  • Florian König
  • Lex van Velsen
  • Alexandros Paramythis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5692)


This paper provides an overview of Prospector, a personalized Internet meta-search engine, which utilizes a combination of ontological information, ratings-based models of user interests, and complementary theme-oriented group models to recommend (through re-ranking) search results obtained from an underlying search engine. Re-ranking brings “closer to the top” those items that are of particular interest to a user or have high relevance to a given theme. A user-based, real-world evaluation has shown that the system is effective in promoting results of interest, but lags behind Google in user acceptance, possibly due to the absence of features popularized by said search engine. Overall, users would consider employing a personalized search engine to perform searches with terms that require disambiguation and / or contextualization.


personalized web search Open Directory Project (ODP) collaborative search user evaluation scrutability adaptive search result re-ranking 


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  1. 1.
    Schwendtner, C., König, F., Paramythis, A.: Prospector: an adaptive front-end to the Google search engine. In: LWA 2006: 14th Workshop on Adaptivity and User Modeling in Interactive System, pp. 56–61. University of Hildesheim, Institute of Computer Science, Hildesheim (2006)Google Scholar
  2. 2.
    Paramythis, A., König, F., Schwendtner, C., Van Velsen, L.: Using thematic ontologies for user- and group-based adaptive personalization in web searching. Paper presented at the 6th International Workshop on Adaptive Multimedia Retrieval, Berlin (2008)Google Scholar
  3. 3.
    Van Velsen, L., Paramythis, A., Van der Geest, T.: User-centered formative evaluation of a personalized internet meta-search engine (in review)Google Scholar
  4. 4.
    Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized Search on the World Wide Web. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web 2007. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Pretschner, A., Gauch, S.: Ontology based personalized search. In: 11th IEEE International Con-ference on Tools with Artificial Intelligence, pp. 391–398. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  6. 6.
    Liu, F., Yu, C., Meng, W.: Personalized web search by mapping user queries to categories. In: 11th international conference on Information and knowledge management, pp. 558–565. ACM, New York (2002)Google Scholar
  7. 7.
    Chirita, P.A., Nejdl, W., Paiu, R., Kohlschütter, C.: Using ODP Metadata to Personalize Search. In: 28th ACM International SIGIR Conference on Research and Development in Information Retrieval, pp. 178–185. ACM, New York (2005)Google Scholar
  8. 8.
    Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: 28th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 449–456. ACM, New York (2005)Google Scholar
  9. 9.
    Tanudjaja, F., Mui, L.: Persona: A contextualized and personalized web search. In: 35th Annual Hawaii International Conference on System Sciences, pp. 67–75. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  10. 10.
    Smyth, B., Balfe, E.: Anonymous personalization in collaborative web search. Information Retrieval 9, 165–190 (2006)CrossRefGoogle Scholar
  11. 11.
    Middleton, S.E., De Roure, D.C., Shadbolt, N.R.: Capturing Knowledge of User Preferences: on-tologies on recommender systems. In: 1st International Conference on Knowledge Capture, pp. 100–107. ACM, New York (2001)Google Scholar
  12. 12.
    Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological User Profiling in Recommender Systems. ACM Transactions on Information Systems 22, 54–88 (2004)CrossRefGoogle Scholar
  13. 13.
    Brusilovsky, P., Millán, E.: User Models for Adaptive Hypermedia and Adaptive Educational Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web 2007. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Anand, S., Mobasher, B.: Intelligent Techniques for Web Personalization. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS, vol. 3169, pp. 1–36. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Coyle, M., Smyth, B.: Supporting intelligent web search. ACM Transactions on Internet Technology 7, 20 (2007)CrossRefGoogle Scholar
  16. 16.
    Kay, J.: Stereotypes, student models and scrutability. In: Gauthier, G., Frasson, C., VanLehn, K. (eds.) ITS 2000. LNCS, vol. 1839, pp. 19–30. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  17. 17.
    Renda, M.E., Umberto, S.: Web metasearch: rank vs. score based rank aggregation methods. In: 2003 ACM symposium on Applied computing, pp. 841–846. ACM, New York (2003)CrossRefGoogle Scholar
  18. 18.
    Shaw, J., Fox, E.: Combination of Multiple Searches. Paper presented at the Text REtrieval Conference, Gaithersburg, USA (1993)Google Scholar
  19. 19.
    Van Velsen, L., König, F., Paramythis, A.: Assessing the Effectiveness and Usability of Personalized Internet Search through a Longitudinal Evaluation. In: 6th Workshop on User-Centred Design and Evaluation of Adaptive Systems, held in conjunction with the International Conference on User Modeling, Adaptation, and Personalization, pp. 44–53 (2009) CEUR-WS.orgGoogle Scholar
  20. 20.
    Paramythis, A., Weibelzahl, S.: A decomposition model for the layered evaluation of interactive adaptive systems. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS, vol. 3538, pp. 438–442. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  21. 21.
    Keane, M.T., O’Brien, M., Smyth, B.: Are people biased in their use of search engines? Communications of the ACM 51, 49–52 (2008)CrossRefGoogle Scholar
  22. 22.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collabora-tive filtering. In: 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufman, San Francisco (1998)Google Scholar
  23. 23.
    Dou, Z., Song, R., Wen, J.: A large-scale evaluation and analysis of personalized search strategies. In: 16th international conference on WWW, pp. 581–590. ACM, New York (2007)Google Scholar
  24. 24.
    Jansen, B.J., Zhang, M., Zhang, Y.: The effect of brand awareness on the evaluation of search engine results. In: CHI 2007 Extended Abstracts on Human Factors in Computing Systems, pp. 2471–2476. ACM, New York (2007)Google Scholar
  25. 25.
    Marchionini, G.: Information seeking in electronic environments. Cambridge University Press, New York (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Florian König
    • 1
  • Lex van Velsen
    • 2
  • Alexandros Paramythis
    • 1
  1. 1.Institute for Information Processing and Microprocessor Technology (FIM)Johannes Kepler UniversityLinzAustria
  2. 2.Dpt. of Technical and Professional CommunicationUniversity of TwenteEnschedeThe Netherlands

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