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

Alexander Felfernig, Müslüm Atas, Thi Ngoc Trang Tran, Martin Stettinger, Seda Polat Erdeniz, Gerhard Leitner

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Translated title of the contributionAn Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items
Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017
Place of PublicationCham
Number of pages10
Publication statusPublished - 27 Jun 2017
EventInternational Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - Université d’Artois, Arras, France
Duration: 27 Jun 201730 Jun 2017

Publication series

NameLecture Notes in Computer Science


ConferenceInternational Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Abbreviated titleIEA/AIE 2017
Internet address


  • recommender systems
  • group decision making
  • group recommendation
  • decision heuristics

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