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<item>
  <id>06094754</id>
  <dt>a</dt>
  <an>06094754</an>
  <augroup>
    <au>Ntoutsi, Irene</au>
    <au>Stefanidis, Kostas</au>
    <au>Norvag, Kjetil</au>
    <au>Kriegel, Hans-Peter</au>
  </augroup>
  <ti>gRecs: a group recommendation system based on user clustering.</ti>
  <so>Lee, Sang-goo (ed.) et al., Database systems for advanced applications. 17th international conference, DASFAA 2012, Busan, South Korea, April 15--19, 2012. Proceedings, Part II. Berlin: Springer (ISBN 978-3-642-29034-3/pbk). Lecture Notes in Computer Science 7239, 299-303 (2012).</so>
  <py>2012</py>
  <pu>Berlin: Springer</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
  <ccgroup>
  </ccgroup>
  <utgroup>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1007/978-3-642-29035-0_25</li>
  </ligroup>
  <abgroup>
    <ab>Summary: In this demonstration paper, we present gRecs, a system for group recommendations that follows a collaborative strategy. We enhance recommendations with the notion of support to model the confidence of the recommendations. Moreover, we propose partitioning users into clusters of similar ones. This way, recommendations for users are produced with respect to the preferences of their cluster members without extensively searching for similar users in the whole user base. Finally, we leverage the power of a top-$k$ algorithm for locating the top-$k$ group recommendations.</ab>
    <rv></rv>
  </abgroup>
</item>