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

doi:10.1007/978-3-319-60042-0_39

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

Institute of Software Technology (7160)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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.

Translated title of the contribution	An Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items
Original language	English
Title of host publication	Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017
Place of Publication	Cham
Publisher	Springer
Pages	335-344
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-319-60042-0_39
Publication status	Published - 27 Jun 2017
Event	International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - Université d’Artois, Arras, France Duration: 27 Jun 2017 → 30 Jun 2017 http://www.cril.univ-artois.fr/ieaaie2017/

Publication series

Name	Lecture Notes in Computer Science
Volume	10350

Conference

Conference	International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Abbreviated title	IEA/AIE 2017
Country/Territory	France
City	Arras
Period	27/06/17 → 30/06/17
Internet address	http://www.cril.univ-artois.fr/ieaaie2017/

Keywords

recommender systems
group decision making
group recommendation
decision heuristics

Access to Document

10.1007/978-3-319-60042-0_39

Cite this

Felfernig, A., Atas, M., Tran, T. N. T., Stettinger, M., Polat Erdeniz, S., & Leitner, G. (2017). An Analysis of Group Recommendation Heuristics for High-and Low-Involvement Items. In Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017 (pp. 335-344). (Lecture Notes in Computer Science; Vol. 10350). Springer. https://doi.org/10.1007/978-3-319-60042-0_39

An Analysis of Group Recommendation Heuristics for High-and Low-Involvement Items. / Felfernig, Alexander; Atas, Müslüm; Tran, Thi Ngoc Trang et al.
Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Cham: Springer, 2017. p. 335-344 (Lecture Notes in Computer Science; Vol. 10350).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Felfernig, A, Atas, M, Tran, TNT , Stettinger, M, Polat Erdeniz, S & Leitner, G 2017, An Analysis of Group Recommendation Heuristics for High-and Low-Involvement Items. in Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science, vol. 10350, Springer, Cham, pp. 335-344, International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Arras, France, 27/06/17. https://doi.org/10.1007/978-3-319-60042-0_39

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

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Keywords

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