Algorithms for Group Recommendation

Research output: Chapter in Book/Report/Conference proceedingChapterResearch


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.
Original languageEnglish
Title of host publicationGroup Recommender Systems
Subtitle of host publicationAn Introduction
Place of PublicationCham
Number of pages32
ISBN (Electronic)978-3-319-75067-5
ISBN (Print)978-3-319-75066-8
Publication statusPublished - 2018

Publication series

NameSpringerBriefs in Electrical and Computer Engineering



  • group recommendation
  • algorithms

Cite this

Felfernig, A., Atas, M., Helic, D., Tran, T. N. T., Stettinger, M., & Samer, R. (2018). Algorithms for Group Recommendation. In Group Recommender Systems: An Introduction (pp. 27-58). (SpringerBriefs in Electrical and Computer Engineering). Cham: Springer.