Abstract
Group recommender systems aim at supporting a group of users in making decisions when considering a set of alternatives. State of the art solutions aggregate individual preferences acquired before the actual decision making process and suggest items that fit the aggregated model. In this paper, we illustrate a different approach, which is implemented in a system that records and uses the users’ preferences expressed while the group discusses options. The system monitors users’ interactions and offers appropriate directions and recommendations. The system runs on a smartphone and acts as a facilitator to guide and help the group members in coming up with an agreement and a final decision. In order to measure the effectiveness of the proposed technologies we have focussed on usability and perceived recommendation quality. In a controlled live user study, we have measured a high usability score, good user-perceived recommendation quality and choice satisfaction.
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
Notes
- 1.
LTS: http://www.lts.it.
References
Ardissono, L., Goy, A., Petrone, G., Segnan, M., & Torasso, P. (2003). Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Applied Artificial Intelligence, 17(8–9), 687–714.
Baltrunas, L., Makcinskas, T., & Ricci, F. (2010). Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems (pp. 119–126). ACM.
Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. International Journal of Human-Computer Interaction, 24(6), 574–594.
Berkovsky, S., & Freyne, J. (2010, September). Group-based recipe recommendations: Analysis of data aggregation strategies. In Proceedings of the fourth ACM conference on Recommender systems (pp. 111–118). ACM.
Braunhofer, M., Elahi, M., & Ricci, F. (2014). Usability assessment of a context-aware and personality-based mobile recommender system. In International conference on electronic commerce and web technologies (pp. 77–88). Springer.
Chen, L., de Gemmis, M., Felfernig, A., Lops, P., Ricci, F., & Semeraro, G. (2013). Human decision making and recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 3(3), 17.
Delic, A., Neidhardt, J., Nguyen, T. N., Ricci, F., Rook, L., Werthner, H., & Zanker, M. (2016, September). Observing group decision making processes. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 147–150). ACM.
Forsyth, D. (2014). Group Dynamics. Wadsworth Cengage Learning (6th ed.).
Guzzi, F., Ricci, F., & Burke, R. (2011, October). Interactive multi-party critiquing for group recommendation. In Proceedings of the Fifth ACM Conference on Recommender Systems (pp. 265–268). ACM.
Jameson, A. (2004, May). More than the sum of its members: Challenges for group recommender systems. In Proceedings of the Working Conference on Advanced Visual Interfaces (pp. 48–54). ACM.
Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The Adaptive Web (pp. 596–627). Springer Berlin Heidelberg.
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4–5), 441–504.
Masthoff, J. (2015). Group recommender systems: Aggregation, satisfaction and group attributes. In Recommender Systems Handbook (pp. 743–776). Springer US.
McCarthy, K., McGinty, L., Smyth, B., & Salamó, M. (2006, September). The needs of the many: A case-based group recommender system. In European Conference on Case-Based Reasoning (pp. 196–210). Springer Berlin Heidelberg.
McGinty, L., & Reilly, J. (2011). On the evolution of critiquing recommenders. In Recommender Systems Handbook (pp. 419–453). Springer US.
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges (pp. 1–34). US: Springer.
Sauro, J., & Lewis, J. R. (2012). Quantifying the user experience: Practical statistics for user research. Elsevier.
Shafir, E., Simonson, I., & Tversky, A. (1993). Reason-based choice. Cognition, 49(1–2), 11–36.
Stettinger, M., Felfernig, A., Leitner, G., Reiterer, S., & Jeran, M. (2015, March). Counteracting serial position effects in the CHOICLA group decision support environment. In Proceedings of the 20th International Conference on Intelligent User Interfaces (pp. 148–157). ACM.
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
Nguyen, T.N., Ricci, F. (2017). A Chat-Based Group Recommender System for Tourism. In: Schegg, R., Stangl, B. (eds) Information and Communication Technologies in Tourism 2017. Springer, Cham. https://doi.org/10.1007/978-3-319-51168-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-51168-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51167-2
Online ISBN: 978-3-319-51168-9
eBook Packages: Business and ManagementBusiness and Management (R0)