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Personalizing Aggregated Search

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

Abstract

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.

Keywords

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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

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