Disambiguating Search by Leveraging a Social Context Based on the Stream of User’s Activity

  • Tomáš Kramár
  • Michal Barla
  • Mária Bieliková
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6075)


Older studies have proved that when searching information on the Web, users tend to write short queries, unconsciously trying to minimize the cognitive load. However, as these short queries are very ambiguous, search engines tend to find the most popular meaning – someone who does not know anything about cascading stylesheets might search for a music band called css and be very surprised about the results. In this paper we propose a method which can infer additional keywords for a search query by leveraging a social network context and a method to build this network from the stream of user’s activity on the Web.


Community Detection Query Expansion Original Query Keyword Extraction Implicit Rating 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jansen, J., Spink, A., Saracevic, T.: Real Life, Real Users, and Real Needs: a Study and Analysis of User Queries on the Web. Information Processing & Management 36(2), 207–227 (2000)CrossRefGoogle Scholar
  2. 2.
    Haveliwala, T.H.: Topic-sensitive Pagerank. In: WWW 2002, pp. 517–526. ACM, New York (2002)CrossRefGoogle Scholar
  3. 3.
    Almeida, R.B., Almeida, V.A.F.: A Community-aware Search Engine. In: WWW 2004 (2004)Google Scholar
  4. 4.
    Bender, M., et al.: Exploiting Social Relations for Query Expansion and Result Ranking. In: ICDEW 2008, pp. 501–506. IEEE, Los Alamitos (2008)Google Scholar
  5. 5.
    Carmel, D., et al.: Automatic Query Refinement using Lexical Affinities with Maximal Information Gain. In: SIGIR 2002, pp. 283–290. ACM, New York (2002)CrossRefGoogle Scholar
  6. 6.
    Liu, S., et al.: An Effective Approach to Document Retrieval via Utilizing WordNet and Recognizing Phrases. In: SIGIR 2004, pp. 266–272. ACM, New York (2004)CrossRefGoogle Scholar
  7. 7.
    Kajaba, M., Návrat, P., Chudá, D.: A simple personalization layer improving relevancy of web search. Computing and Information Systems Journal 13, 29–35 (2009)Google Scholar
  8. 8.
    Barla, M., Bieliková, M.: “Wild” Web Personalization: Adaptive Proxy Server. In: Workshop on Intelligent and Knowledge Oriented Tech., WIKT 2009, pp. 48–51 (2009)Google Scholar
  9. 9.
    Barla, M., Bieliková, M.: On Deriving Tagsonomies: Keyword Relations Coming from Crowd. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 309–320. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tomáš Kramár
    • 1
  • Michal Barla
    • 1
  • Mária Bieliková
    • 1
  1. 1.Faculty of Informatics and Information TechnologySlovak University of TechnologyBratislavaSlovakia

Personalised recommendations