Algorithms for Group Recommendation

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)


In this chapter, our aim is to show how group recommendation can be implemented on the basis of recommendation paradigms for individual users. Specifically, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) aggregated predictions and (2) aggregated models as basic strategies for aggregating the preferences of individual group members.


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Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.Institute for Software TechnologyGraz University of TechnologyGrazAustria
  2. 2.EURECATCentre Tecnológico de CatalunyaBarcelonaSpain
  3. 3.Faculty of Computer ScienceFree University of Bozen-BolzanoBolzanoItaly

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