Personalizing Aggregated Search

  • Stanislav Makeev
  • Andrey Plakhov
  • Pavel Serdyukov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


Aggregated search nowadays has become a widespread technique of generating Search Engine Result Page (SERP). The main task of aggregated search is incorporating results from a number of specialized search collections (often referenced as verticals) into a ranked list of Web-search results. To proceed with the blending algorithm one can use a variety of different sources of information starting from some textual features up to query-log data. In this paper we study the usefulness of personalized features for improving the quality of aggregated search ranking. The study is carried out by training a number of machine-learned blending algorithms, which differ by the sets of used features. Thus we not only measure the value of personalized approach in the aggregated search context, but also find out which classes of personalized features outperform the others.


Feature Vector Ranking Function Query Stream User Pool Query Likelihood 
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.
    Arguello, J., Diaz, F., Callan, J.: Learning to aggregate vertical results into web search result. In: Proceedings of CIKM 2011(2011)Google Scholar
  2. 2.
    Arguello, J., Diaz, F., Callan, J., Crespo, J.F.: Sources of evidence for vertical selection. In: Proceedings of SIGIR 2009 (2009)Google Scholar
  3. 3.
    Arguello, J., Diaz, F., Paiement, J.F.: Vertical selection in the presence of unlabeled verticals. In: Proceedings of SIGIR 2010 (2010)Google Scholar
  4. 4.
    Bennett, P.N., Radlinski, F., White, R.W., Yilmaz, E.: Inferring and using location metadata to personalize web search. In: Proceedings of SIGIR 2011 (2011)Google Scholar
  5. 5.
    Bennett, P.N., White, R.W., Chu, W., Dumais, S.T., Bailey, P., Borisyuk, F., Cui, X.: Modeling the impact of short- and long-term behavior on search personalization. In: Proceedings of SIGIR 2012 (2012)Google Scholar
  6. 6.
    Collins-Thompson, K., Bennett, P.N., White, R.W., de la Chica, S., Sontag, D.: Personalizing web search results by reading level. In: Proceedings of CIKM 2011 (2011)Google Scholar
  7. 7.
    Diaz, F.: Integration of news content into web results. In: Proceedings of WSDM 2009 (2009)Google Scholar
  8. 8.
    Diaz, F., Arguello, J.: Adaptation of offline vertical selection predictions in the presence of user feedback. In: Proceedings of SIGIR 2009 (2009)Google Scholar
  9. 9.
    Dou, Z., Song, R., Wen, J.R.: A large-scale evaluation and analysis of personalized search strategies. In: Proceedings of WWW 2007 (2007)Google Scholar
  10. 10.
    Kharitonov, E., Serdyukov, P.: Gender-aware re-ranking. In: Proceedings of SIGIR 2012 (2012)Google Scholar
  11. 11.
    Ponnuswami, A., Pattabiraman, K., Wu, Q., Gilad-Bachrach, R., Kanungo, T.: On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. In: Proceedings of WSDM 2011 (2011)Google Scholar
  12. 12.
    Styskin, A., Romanenko, F., Vorobyev, F., Serdyukov, P.: Recency ranking by diversification of result set. In: Proceedings of CIKM 2011 (2011)Google Scholar
  13. 13.
    Vallet, D., Castells, P.: Personalized diversification of search results. In: Proceedings of SIGIR 2012 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Stanislav Makeev
    • 1
  • Andrey Plakhov
    • 1
  • Pavel Serdyukov
    • 1
  1. 1.YandexMoscowRussia

Personalised recommendations