Research Methods for Group Recommender Systems
Type:
Speech with proceedings
Proceedings:
Proceedings of the Workshop on Recommenders in Tourism (RecTour 2016)
Publisher:
CEUR-WS.org
Pages:
30 - 37
ISBN:
ISSN: 1613-0073
Year:
2016
Abstract:
In this article we argue that the research on group recom- mender systems must look more carefully at group dynamics in decision making in order to produce technologies that will be truly beneficial for users. Hence, we illustrate a user study method aimed at observing and measuring the evolution of user preferences and actions in a tourism decision making task: finding a destination to visit. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings may have on the design of interactive group recommender systems.
TU Focus:
Information and Communication Technology
Reference:
A. Delic, J. Neidhardt, T. Nguyen, F. Ricci:
"Research Methods for Group Recommender Systems";
Vortrag: RecTour 2016 - Workshop on Recommenders in Tourism at ACM RecSys 2016, Boston, MA, USA; 15.09.2016; in: "Proceedings of the Workshop on Recommenders in Tourism (RecTour 2016)", CEUR-WS.org, Boston, MA, USA (2016), ISSN: 1613-0073; S. 30 - 37.
Zusätzliche Informationen
PDF Link:
Last changed:
27.06.2018 11:26:20
TU Id:
250531
Accepted:
Accepted
Invited:
Department Focus:
Business Informatics
Info Link:
https://publik.tuwien.ac.at/showentry.php?ID=250531&lang=1
Abstract German:
In this article we argue that the research on group recom- mender systems must look more carefully at group dynamics in decision making in order to produce technologies that will be truly beneficial for users. Hence, we illustrate a user study method aimed at observing and measuring the evolution of user preferences and actions in a tourism decision making task: finding a destination to visit. We discuss the benefits and caveats of such an observational study method and we present the implications that the derived data and findings may have on the design of interactive group recommender systems.