188/4 E-Commerce Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology
Favoritenstrasse 9-11/188, A-1040 Vienna, Austria

Research Methods for Group Recommender Systems

Type: 
Speech with proceedings
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.
Hidden Keywords: 
Department Focus: 
Business Informatics
TU Focus: 
Information and Communication Technology
Proceedings: 
Proceedings of the Workshop on Recommenders in Tourism (RecTour 2016)
Publisher: 
CEUR-WS.org
Year: 
2016
ISBN: 
ISSN: 1613-0073
Pages: 
30 - 37
Accepted: 
Acceptance undecided
Invited: 
Reference: 
<u>A. Delic</u>, J. Neidhardt, T. Nguyen, F. Ricci: <br>"<i><a href="http://ceur-ws.org/Vol-1685/paper5.pdf" target="_blank" class="publist">Research Methods for Group Recommender Systems</a></i>"; <br>Talk: RecTour 2016 - Workshop on Recommenders in Tourism at ACM RecSys 2016, Boston, MA, USA; 09-15-2016; in: "<i>Proceedings of the Workshop on Recommenders in Tourism (RecTour 2016)</i>", CEUR-WS.org, Boston, MA, USA (2016), ISSN: 1613-0073; 30 - 37.<br><br> <a href="https://publik.tuwien.ac.at/showentry.php?ID=250531&lang=2" class="publist"><i>More information</i></a><br><br>
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.