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

Titel in Übersetzung: An Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

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.
Originalspracheenglisch
TitelAdvances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017
ErscheinungsortCham
Herausgeber (Verlag)Springer
Seiten335-344
Seitenumfang10
DOIs
PublikationsstatusVeröffentlicht - 27 Jun 2017
VeranstaltungInternational Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - Université d’Artois, Arras, Frankreich
Dauer: 27 Jun 201730 Jun 2017
http://www.cril.univ-artois.fr/ieaaie2017/

Publikationsreihe

NameLecture Notes in Computer Science
Band10350

Konferenz

KonferenzInternational Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
KurztitelIEA/AIE 2017
LandFrankreich
OrtArras
Zeitraum27/06/1730/06/17
Internetadresse

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