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?

Previous theme park research

Fairground: Thrill Laboratory (ACE 2007 and CHI 2008)

Previous theme park research

Bucking Bronco: Ride Experiment Number 1 (ACE 2009)

A interesting research question

How might we design a computational system that supports the enjoyment of visitors to theme parks?

Why?

• ~$200 entry per family

• Food

• Accommodation

• Travel

• Limited time at the park

• Queues …

• Many first time visitors

Existing uses of profiling

A narrower research question

To what extent can a visitor profile predict experiences in the theme park?

Big Five

Extraversion: 7/10Agreeableness: 6/10Conscientiousness: 4/10Openness: 9/10Neuroticism: 3/10

Profile design

Sensation Seeking Scale

Thrill-seeking: 8/10Experience-seeking: 9/10Disinhibition: 6/10Boredom-susceptibility: 4/10Sensation-seeking: 6.8/10

Demographics: Age, gender, previous ride experience

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?

Oblivion: Thrill Laboratory

• 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!

A proof of concept investigation

Big FiveExtraversion, Openness

Sensation Seeking ScaleThrill seeking

DemographicsPrevious ride experience

Significant dimensions

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!

Clustering

CS1

Ride experience0 10

CS2

Thrill Seeking0 10

CS3

Extraversion0 10

0

10

Ope

nnes

s

• Studies across multiple rides

• Rigorous sampling method

• Investigating other profiling methodologies

• Implementation and assessment of recommendation system

• Investigation of potential business models

Future work

• 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?

Implications for research