Recommender algorithms in activity motivating games

Shlomo Berkovsky, Jill Freyne, Mac Coombe, Dipak Bhandari

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

13 Citations (Scopus)

Abstract

Physical activity motivating game design encourages players to perform real physical activity in order to gain virtual game rewards. Previous research into activity motivating games showed that they have the potential to motivate players to perform physical activity, while retaining the enjoyment of playing. However, it was discovered that a uniform motivating approach resulted in different levels of activity performed by players of varying gaming skills. In this work we present and evaluate two adaptive recommendation-based techniques, which aim to balance the amount of physical activity performed by players by adapting the level of motivation to their observed gaming skills. Experimental evaluation showed that the adaptive techniques not only increase the amount of activity performed and retain the enjoyment of playing, but also balance the amount of activity performed by players of varying gaming skills and allow for game difficulty to be set in a player-dependent manner.
Original languageEnglish
Title of host publicationProceedings of the fourth ACM conference on Recommender systems, RecSys '10
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages175-182
Number of pages8
ISBN (Electronic)9781605589060
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event4th ACM Conference on Recommender Systems, RecSys 2010 - Barcelona, Spain
Duration: 26 Sep 201030 Sep 2010

Conference

Conference4th ACM Conference on Recommender Systems, RecSys 2010
CountrySpain
CityBarcelona
Period26/09/1030/09/10

Keywords

  • Recommendation algorithms
  • games
  • player adaptivity
  • physical activity
  • user study

Cite this

Berkovsky, S., Freyne, J., Coombe, M., & Bhandari, D. (2010). Recommender algorithms in activity motivating games. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10 (pp. 175-182). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/1864708.1864742