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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The work presented in this paper has been partially conducted within the scope of the research projects WeWant (basic research project funded by the Austrian Research Promotion Agency) and OpenReq (Horizon 2020 project funded by the European Union).
- 2.
Graz University of Technology (http://www.tugraz.at) and Alpen-Adria Universität Klagenfurt (http://www.aau.at).
References
Herlocker, J., Terveen, L., Konstan, J., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 596–627. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72079-9_20
Jameson, A.: More than the sum of its members: challenges for group recommender systems. In: Working Conference on Advanced Visual Interfaces, AVI 2004, pp. 48–54. ACM (2004)
Jameson, A., Willemsen, M.C., Felfernig, A., Gemmis, M., Lops, P., Semeraro, G., Chen, L.: Human decision making and recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 611–648. Springer, Boston, MA (2015). doi:10.1007/978-1-4899-7637-6_18
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010)
Masthoff, J.: Group modeling: selecting a sequence of television items to suit a group of viewers. UMUAI 14(1), 37–85 (2004)
Masthoff, J.: The user as wizard: A method for early involvement in the design and evaluation of adaptive systems. In: 5th Workshop on User-Centered Design and Evaluation of Adaptive Systems (2006)
Masthoff, J.: Group recommender systems: combining individual models. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 677–702. Springer, Heidelberg (2011)
McCarthy, K., Salamo, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: Group recommender systems: a critiquing based approach. In: IUI 2006. ACM, pp. 267–269 (2006)
Ninaus, G., Felfernig, A., Stettinger, M., Reiterer, S., Leitner, G., Weninger, L., Schanil, W.: Intelligent techniques for software requirements engineering. In: In European Conference on AI, Prestigious Applications of Intelligent Systems (PAIS), pp. 1161–1166 (2014)
O’Connor, M., Cosley, D., Konstan, J., Riedl, J.: PolyLens: A recommmender system for groups of users. In: European Conference on Computer-Supported Cooperative Work, pp. 199–218. ACM (2001)
Payne, J., Bettman, J., Johnson, E.: The Adaptive Decision Maker. Campridge University Press, New York (1993)
Petty, R., Cacioppo, J., Schumann, D.: Central and peripheral routes to advertising effectiveness: the moderating role of involvement. J. Consum. Res. 10, 135–146 (1983)
Simon, H.: A behavioral model of rational choice. Q. J. Economics 69, 99–118 (1955)
Stettinger, M., Felfernig, A., Leitner, G., Reiterer, S.: Counteracting anchoring effects in group decision making. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds.) UMAP 2015. LNCS, vol. 9146, pp. 118–130. Springer, Cham (2015). doi:10.1007/978-3-319-20267-9_10
Stettinger, M., Felfernig, A., Leitner, G., Reiterer, S., Jeran, M.: Counteracting serial position effects in the CHOICLA group decision support environment. In: 20th ACM Conference on Intelligent User Interfaces (IUI 2015), Atlanta, Georgia, USA, pp. 148–157. ACM (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Felfernig, A., Atas, M., Tran, T.N.T., Stettinger, M., Erdeniz, S.P., Leitner, G. (2017). An Analysis of Group Recommendation Heuristics for High- and Low-Involvement Items. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-60042-0_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60041-3
Online ISBN: 978-3-319-60042-0
eBook Packages: Computer ScienceComputer Science (R0)