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Recommending rides: Psychometric profiling in the theme park

Recommending rides: Psychometric profiling in the theme park. Stefan Rennick Egglestone , Amanda Whitbrook, Julie Greensmith, Brendan Walker, Steve Benford, Joe Marshall, David Kirk, Ainoje Irune and Duncan Rowland. Why theme park research?. Why theme park research?.

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Recommending rides: Psychometric profiling in the theme park

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  1. Recommending rides: Psychometric profiling in the theme park Stefan Rennick Egglestone, Amanda Whitbrook, Julie Greensmith, Brendan Walker, Steve Benford, Joe Marshall, David Kirk, Ainoje Irune and Duncan Rowland

  2. Why theme park research?

  3. Why theme park research?

  4. Previous theme park research Fairground: Thrill Laboratory (ACE 2007 and CHI 2008)

  5. Previous theme park research Bucking Bronco: Ride Experiment Number 1 (ACE 2009)

  6. A interesting research question How might we design a computational system that supports the enjoyment of visitors to theme parks?

  7. Why? • ~$200 entry per family • Food • Accommodation • Travel • Limited time at the park • Queues … • Many first time visitors

  8. Existing uses of profiling

  9. A narrower research question To what extent can a visitor profile predict experiences in the theme park?

  10. Profile design Big Five Extraversion: 7/10 Agreeableness: 6/10 Conscientiousness: 4/10 Openness: 9/10 Neuroticism: 3/10 Demographics: Age, gender, previous ride experience Sensation Seeking Scale Thrill-seeking: 8/10 Experience-seeking: 9/10 Disinhibition: 6/10 Boredom-susceptibility: 4/10 Sensation-seeking: 6.8/10

  11. Collection of experience reports The circumplex model Arousal: How much do you feel alert, with your body pumped up and buzzing, ready for action? Valence: How much do you feel positive and good, or negative and bad?

  12. Oblivion: Thrill Laboratory

  13. A proof of concept investigation • Identify profiling dimensions with a relationship to self-reports of experience • Cluster participants using these dimensions • Test for a statistically-significant difference in self-reported experience between clusters • Full details in paper!

  14. Significant dimensions Big Five Extraversion, Openness Sensation Seeking Scale Thrill seeking Demographics Previous ride experience

  15. Clustering Three cluster sets generated using k-means method • CS1: Previous ride experience • CS2: Thrill seeking • CS3: Extraversion and Openness Significant differences in self-reports of experience between cluster membership!

  16. CS1 0 10 Ride experience

  17. CS2 0 10 Thrill Seeking

  18. CS3 10 Openness 0 0 10 Extraversion

  19. Future work • Studies across multiple rides • Rigorous sampling method • Investigating other profiling methodologies • Implementation and assessment of recommendation system • Investigation of potential business models

  20. Implications for research • Psychometrics interesting in settings where personality is a mediator of experience • Requires the user to invest substantial amounts of time (eg ~30 minutes to fill out two questionnaires) • What other applications are there?

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