Towards Contextual Search: Social Networks, Short Contexts and Multiple Personas

  • Tomáš Kramár
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)


In this paper we present an approach to contextual search, based on the automatically extracted metadata from visited documents. User model represents user’s interests as a combination of tags, keywords and named entities. Such user model is further enhanced by automatically detected communities of similar users, based on the similarities of their models. The user may belong to multiple communities, each representing one of her possibly many personas – roles or stereotypes, facets of her interests. We discuss further possibilities of using this model to bring more fine-grained contextualization and search improvement by using short contexts.


personalized search search context personas social networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anagnostopoulos, L., Becchetti, C.: Castillo, and A. Gionis, An optimization framework for query recommendation. In: Web Search and Web Data Mining, pp. 161–170 (2010)Google Scholar
  2. 2.
    Haveliwala, T.H.: Topic-sensitive PageRank. In: Proc. of Int. Conf. on World Wide Web (WWW 2002), pp. 517–526. ACM, New York (2002)Google Scholar
  3. 3.
    Qiu, F., Cho, J.: Automatic identification of user interest for personalized search. In: Proc. of Int. World Wide Web Conference (WWW 2006), pp. 727–736 (2006)Google Scholar
  4. 4.
    Xiang, et al.: Context-aware ranking in web search. In: Proc. of Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 451–458. ACM, New York (2010)Google Scholar
  5. 5.
    Wetzker, R., Zimmermann, C., Bauckhage, C., Albayrak, S.: I tag, you tag: translating tags for advanced user models. In: Web Search and Web Data Mining, pp. 71–80 (2010)Google Scholar
  6. 6.
    Teevan, J., Morris, M.R., Bush, S.: Discovering and using groups to improve personalized search. In: Web Search and Web Data Mining, pp. 15–24 (2009)Google Scholar
  7. 7.
    White, R., Bennett, P., Dumais, S.: Predicting short-term interests using activity-based search context. In: Information and Knowledge Management, pp. 1009–1018. ACM, New York (2010)Google Scholar
  8. 8.
    Kramár, T., Barla, M., Bieliková, M.: Disambiguating search by leveraging a social context based on the stream of user’s activity. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 387–392. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Kramár, T., Barla, M., Bieliková, M.: Adapive proxy server: Operation and experiences after one year. In: Proc. of the 5th Workshop on Intelligent and Knowledge Oriented Technologies (WIKT 2010), Equilibria, pp. 48–51 (2010)Google Scholar
  10. 10.
    Kramár, T., Barla, M., Bieliková, M.: Open-web User Modeling. In: Proc. of Znalosti 2011, pp. 112–123 (2011)Google Scholar
  11. 11.
    Barla, M., Bieliková, M.: Ordinary Web Pages as a Source for Metadata Acquisition for Open Corpus User Modeling. In: Proc. of WWW/Internet, pp. 227–233. IADIS Press (2010)Google Scholar
  12. 12.
    Holub, M., Bieliková, M.: Behavior Based Adaptive Navigation Support. In: Proc. of the Workshop on the Practical Use of Recommender Systems, Algorithms and Technologies (PRSAT 2010). CEUR, vol. 676, pp. 47–50 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tomáš Kramár
    • 1
  1. 1.Institute of Informatics and Software Engineering, Faculty of Informatics and Information TechnologiesSlovak University of TechnologyBratislavaSlovakia

Personalised recommendations