Personalizing the theme park: psychometric profiling and physiological monitoring

Rennick-Egglestone, Stefan; Whitbrook, Amanda; Leygue, Caroline; Greensmith, Julie; Walker, Brendan; Benford, Steve; Schn�delbach, Holger; Reeves, Stuart; Marshall, Joe; Kirk, David; Tennent, Paul; Irune, Ainoje; Rowland, Duncan

Abstract

Theme parks are important and complex forms of entertainment, with a broad user-base, and with a substantial economic impact. In this paper, we present a case study of an existing theme park, and use this to motivate two research challenges in relation to user-modeling and personalization in this environment: developing recommender systems to support theme park visits, and developing rides that are personalized to the users who take part in them. We then provide an analysis, drawn from a real-world study on an existing ride, which illustrates the efficacy of psychometric profiling and physiological monitoring in relation to these challenges. We conclude by discussing further research work that could be carried out within the theme park, but motivate this research by considering the broader contribution to user-modeling issues that it could make. As such, we present the theme park as a microcosm which is amenable to research, but which is relevant in a much broader setting.

Citation

Rennick-Egglestone, S., Whitbrook, A., Leygue, C., Greensmith, J., Walker, B., Benford, S., Schnädelbach, H., Reeves, S., Marshall, J., Kirk, D., Tennent, P., Irune, A., & Rowland, D. (2011, July). Personalizing the theme park: psychometric profiling and physiological monitoring. Presented at 19th International Conference, UMAP 2011, Girona, Spain

• Presentation Conference Type: Edited Proceedings
• Conference Name: 19th International Conference, UMAP 2011
• Start Date: Jul 11, 2011
• End Date: Jul 15, 2011
• Publication Date: Jul 5, 2011
• Deposit Date: Nov 25, 2011
• Publicly Available Date: Nov 25, 2011
• Peer Reviewed: Peer Reviewed
• Pages: 281–292
• Series Title: Lecture notes in computer science
• Series Number: 6787
• Series ISSN: 1611-3349
• Book Title: User Modeling, Adaptation and Personalization
• ISBN: 9783642223617
• DOI: https://doi.org/10.1007/978-3-642-22362-4_24
• Public URL: https://nottingham-repository.worktribe.com/output/707836
• Publisher URL: http://www.springerlink.com/content/f1w7mkn711302527/