Skip to main content

A CBR-Based Game Recommender for Rehabilitation Videogames in Social Networks

  • Conference paper
Intelligent Data Engineering and Automated Learning – IDEAL 2014 (IDEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8669))

Abstract

Health care can be greatly improved through social activities. Present day technology can help through social networks and free internet games. A system can be built, combining present day technology with recommender systems to ensure supervision for the elderly and disabled. Using the behavior studied on social networking sites a system was created to match games to particular users. Common associations between a user’s personality and a game’s genre were considered in the process and used to create a formula for how appropriate a game suggestion is. We found that the games receiving the best results from the users were those games that trained those certain users’ disabilities not others.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Classification, Assessment, Surveys and Terminology Team, International Classification of Functioning, Disability and Health. World Health Organization (2001)

    Google Scholar 

  2. Emiliani, P.L.: Assistive Technology (AT) versus Mainstream Technology (MST): The research perspective. Institute of applied Physics, National Research Council, Firenze, Italy (2006)

    Google Scholar 

  3. Shye, D., Mullooly, J.P., Freeborn, D.K., Pope, C.R.: Gender Differences in the relationship between social networks support and mortality: A longitudinal study of an elderly cohort. Elsevier Science Ltd. (1995)

    Google Scholar 

  4. Holmén, K., Furukawa, H.: Loneliness, health and social network among elderly people – a follow-up study. Archives of Gerontology and Geriatrics 35 (2002)

    Google Scholar 

  5. de Belvis, A.G., Avolio, M., Spagnolo, A., Damiani, G., Sicuro, L., Cicchetti, A., Ricciardi, W., Rosano, A.: Factors associated with health-related quality of life: the role of social relationships among the elderly in an Italian region. Public Health 122 (2007)

    Google Scholar 

  6. Thomas, P.A.: Gender, social engagement, and limitations in late life. University of Texas at Austin, Texas (2011)

    Google Scholar 

  7. Fiorillo, D., Sabatini, F.: Quality and quantity: The role of social interactions in self-reported individual health. Social Science & Medicine 73 (2011)

    Google Scholar 

  8. Fritsch, T., Steinke, F., Brem, D.: Analysis of Elderly Persons’ Social Network: Need for an Appropriate Online Platform. Association for the Advancement of Artificial Intelligence (2012)

    Google Scholar 

  9. Kickbusch, I., Brindley, C.: Health in the post-2015 development agenda: an analysis of the UN-led thematic consultations, High-Level Panel report and sustainable development debate in the context of health. World Health Organization (2013)

    Google Scholar 

  10. Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)

    Article  Google Scholar 

  11. Zhou, X., Xu, Y., Li, Y., Josang, A., Cox, C.: The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review 37(2), 119–132 (2012)

    Article  Google Scholar 

  12. Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  13. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  15. Chesñevar, C., Maguitman, A., González, M.: Empowering Recommendation Technologies Through Argumentation, pp. 403–422. Springer (2009)

    Google Scholar 

  16. Jitapunkul, S., Pillay, I., Ebrahim, S.: The abbreviated mental test: its use and validity. Age Ageing (1991)

    Google Scholar 

  17. Adali, S., Golbeck, J.: Predicting Personality with Social Behavior

    Google Scholar 

  18. Zammitto, V.L.: Gamers’ Personality and their Gaming Preferences. University of Belgrano (2001)

    Google Scholar 

  19. Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations and system approaches. AI Communications 7(1), 39–59 (1994)

    Google Scholar 

  20. JColibri, http://gaia.fdi.ucm.es/research/colibri/jcolibri

  21. LensKit, http://lenskit.grouplens.org/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Catalá, L., Julián, V., Gil-Gómez, JA. (2014). A CBR-Based Game Recommender for Rehabilitation Videogames in Social Networks. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10840-7_45

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10839-1

  • Online ISBN: 978-3-319-10840-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics