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

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

Publikation: KonferenzbeitragPaperForschungBegutachtung

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.
Titel in ÜbersetzungAn Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items
Originalspracheenglisch
Seiten335-344
Seitenumfang10
PublikationsstatusVeröffentlicht - 15 Aug 2017

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    An Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items. / Felfernig, Alexander; Atas, Müslüm; Tran, Trang; Stettinger, Martin; Polat Erdeniz, Seda; Leitner, Gerhard.

    2017. 335-344.

    Publikation: KonferenzbeitragPaperForschungBegutachtung

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