poster

Analysing user behaviour through dynamic population models

Authors:

Matthew Higgs,

Alistair Morrison,

Mark Girolami,

Matthew ChalmersAuthors Info & Claims

CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems

Pages 271 - 276

https://doi.org/10.1145/2468356.2468405

Published: 27 April 2013 Publication History

Get Access

Abstract

We apply a statistical modelling-based approach to exploring, analysing and predicting behavioural patterns of users of mobile software. The technique employed represents the behaviour of each user through a weighted mixture over data-generating distributions. In the described pilot study, we show how we have modelled the behaviour of over a hundred users of an iOS game. We illustrate how this modelling approach can be used to determine user play strategies and learning rates and show how this affects the length of time users keep returning to play the game. We describe our ongoing work, including feeding results of the modelling into the design process.

References

[1]

Barkhuus, L., Chalmers, M., Tennent, P., Hall, M., Bell, M., Sherwood, S., and Brown, B. Picking pockets on the lawn: The development of tactics and strategies in a mobile game. In Proceedings of UbiComp 2005, Springer (2005), 358--374.

Digital Library

Google Scholar

[2]

Girolami, M., and Kaban, A. Simplicial mixtures of markov chains: Distributed modelling of dynamic user profiles. In Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. MIT Press, Cambridge, MA, 2004.

Google Scholar

[3]

Hofmann, T. Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. 42, 1--2 (Jan. 2001), 177--196.

Digital Library

Google Scholar

[4]

McMillan, D., Morrison, A., Brown, O., Hall, M., and Chalmers, M. Furthersdfsd into the wild: running worldwide trials of mobile systems. In Proceedings of the 8th international conference on Pervasive Computing, Pervasive'10, Springer-Verlag (Berlin, Heidelberg, 2010), 210--227.

Digital Library

Google Scholar

Cited By

View all

Brown BBødker SHöök K(2017)Does HCI scale?Interactions10.1145/312538724:5(28-33)Online publication date: 25-Aug-2017
https://dl.acm.org/doi/10.1145/3125387
Harpstead EZimmermann TNagapan NGuajardo JCooper RSolberg TGreenawalt DCox ACairns PBernhaupt RNacke L(2015)What Drives PeopleProceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play10.1145/2793107.2793114(369-379)Online publication date: 5-Oct-2015
https://dl.acm.org/doi/10.1145/2793107.2793114
Virtanen SRost MHiggs MMorrison AChalmers MGirolami M(2015)Non‐parametric Bayes to infer playing strategies adopted in a population of mobile gamersStat10.1002/sta4.754:1(46-58)Online publication date: 4-Mar-2015
https://doi.org/10.1002/sta4.75
Show More Cited By

Index Terms

Analysing user behaviour through dynamic population models
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Behaviour Models-Enriched User Personas for Digital Behaviour Change Interventions
PETRA '24: Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments

User Persona is a structured Interaction Design method for representing a group of users as conceptual models in text and pictorial formats. The regular user Persona descriptions are limited for designing Digital Behaviour Change Interventions for ...
Non-sequential behaviour of dynamic nets
ICATPN'06: Proceedings of the 27th international conference on Applications and Theory of Petri Nets and Other Models of Concurrency

Dynamic nets are an extension of Petri nets where the net topology may change dynamically. This is achieved by allowing (i) tokens to be coloured with place names (carried on as data), (ii) transitions to designate places where to spawn new tokens on ...
A model for analysing the collective dynamic behaviour and characterising the exploitation of population-based algorithms

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems

April 2013

3360 pages

ISBN:9781450319522

DOI:10.1145/2468356

General Chair:
Wendy E. Mackay
INRIA
,
Program Chairs:
Stephen Brewster
Glasgow University
,
Susanne Bødker
University of Aarhus

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 April 2013

Check for updates

Author Tags

Qualifiers

Poster

Conference

CHI '13

Sponsor:

SIGCHI

CHI '13: CHI Conference on Human Factors in Computing Systems

April 27 - May 2, 2013

Paris, France

Acceptance Rates

CHI EA '13 Paper Acceptance Rate 630 of 1,963 submissions, 32%;

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
178
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Brown BBødker SHöök K(2017)Does HCI scale?Interactions10.1145/312538724:5(28-33)Online publication date: 25-Aug-2017
https://dl.acm.org/doi/10.1145/3125387
Harpstead EZimmermann TNagapan NGuajardo JCooper RSolberg TGreenawalt DCox ACairns PBernhaupt RNacke L(2015)What Drives PeopleProceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play10.1145/2793107.2793114(369-379)Online publication date: 5-Oct-2015
https://dl.acm.org/doi/10.1145/2793107.2793114
Virtanen SRost MHiggs MMorrison AChalmers MGirolami M(2015)Non‐parametric Bayes to infer playing strategies adopted in a population of mobile gamersStat10.1002/sta4.754:1(46-58)Online publication date: 4-Mar-2015
https://doi.org/10.1002/sta4.75
Andrei OCalder MHiggs MGirolami M(2014)Probabilistic Model Checking of DTMC Models of User Activity PatternsQuantitative Evaluation of Systems10.1007/978-3-319-10696-0_11(138-153)Online publication date: 2014
https://doi.org/10.1007/978-3-319-10696-0_11

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Behaviour Models-Enriched User Personas for Digital Behaviour Change Interventions

Non-sequential behaviour of dynamic nets

A model for analysing the collective dynamic behaviour and characterising the exploitation of population-based algorithms