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An Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items

  • Alexander Felfernig
  • Muesluem AtasEmail author
  • Thi Ngoc Trang Tran
  • Martin Stettinger
  • Seda Polat Erdeniz
  • Gerhard Leitner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10350)

Abstract

Group recommender systems are based on aggregation heuristics that help to determine a recommendation for a group. These heuristics aggregate the preferences of individual users in order to reflect the preferences of the whole group. There exist a couple of different aggregation heuristics (e.g., most pleasure, least misery, and average voting) that are applied in group recommendation scenarios. However, to some extent it is still unclear which heuristics should be applied in which context. In this paper, we analyze the impact of the item domain (low involvement vs. high involvement) on the appropriateness of aggregation heuristics (we use restaurants as an example of low-involvement items and shared apartments as an example of high-involvement ones). The results of our study show that aggregation heuristics in group recommendation should be tailored to the underlying item domain.

Keywords

Recommender systems Group decision making Group recommendation Decision heuristics 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alexander Felfernig
    • 1
  • Muesluem Atas
    • 1
    Email author
  • Thi Ngoc Trang Tran
    • 1
  • Martin Stettinger
    • 1
  • Seda Polat Erdeniz
    • 1
  • Gerhard Leitner
    • 2
  1. 1.Institute of Software TechnologyGraz University of TechnologyGrazAustria
  2. 2.Institute for Informatics SystemsAlpen-Adria-Universität KlagenfurtKlagenfurtAustria

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