Research Methods for Group Recommender Systems

Authors: 
Amra Delic
Julia Neidhardt
Thuy Ngoc Nguyen
Francesco Ricci
Type: 
Speech with proceedings
Proceedings: 
Proceedings of the Workshop on Recommenders in Tourism (RecTour 2016)
Publisher: 
CEUR-WS.org
Pages: 
30 - 37
Year: 
2016
ISBN: 
ISSN: 1613-0073
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

Last changed: 
27.06.2018 11:26:20
Accepted: 
Accepted
TU Id: 
250531
Invited: 
Department Focus: 
Business Informatics
Author List: 
A. Delic, J. Neidhardt, T. Nguyen, F. Ricci
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.