Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

NudgeAlong: A Case Based Approach to Changing User Behaviour

  • Conference paper
Case-Based Reasoning Research and Development (ICCBR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8765))

Included in the following conference series:

  • 1229 Accesses

Abstract

Companies want to change the way that users interact with their services. One of the main ways to do this is through messaging. It is well known that different users are likely to respond to different types of messages. Targeting the right message type at the right user is key to achieving successful behaviour change. This paper frames this as a case based reasoning problem. The case representation captures a summary of a user’s interactions with a company’s services over time. The case solution represents a message type that resulted in a desired change in the user’s behaviour. This paper describes this framework, how it has been tested using simulation and a short description of a test deployment.

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
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Fogg, B.: A behavior model for persuasive design. In: Proceedings of the 4th International Conference on Persuasive Technology, p. 40. ACM (2009)

    Google Scholar 

  2. Fogg, B.: Captology: The study of computers as persuasive technologies. In: CHI 1997 Extended Abstracts on Human Factors in Computing Systems, CHI EA 1997, p. 129. ACM, New York (1997)

    Google Scholar 

  3. Jonsson, P., Wohlin, C.: An evaluation of k-nearest neighbour imputation using likert data. In: Proceedings of the 10th International Symposium on Software Metrics, METRICS 2004, pp. 108–118. IEEE Computer Society, Washington, DC (2004)

    Chapter  Google Scholar 

  4. 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 

  5. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artificial Intellegence 2009 (2009)

    Google Scholar 

  6. Victor, P., Cornelis, C., De Cock, M., Teredesai, A.: Trust- and distrust-based recommendations for controversial reviews. IEEE Intelligent Systems 26(1), 48–55 (2011)

    Article  Google Scholar 

  7. Jawaheer, G., Szomszor, M., Kostkova, P.: Characterisation of explicit feedback in an online music recommendation service. In: Amatriain, X., Torrens, M., Resnick, P., Zanker, M. (eds.) RecSys, pp. 317–320. ACM (2010)

    Google Scholar 

  8. Palanivel, K., Sivakumar, R.: A study on collaborative recommender system using fuzzy-multicriteria approaches. IJBIS 7(4), 419–439 (2011)

    Article  Google Scholar 

  9. Cordier, A., Lefevre, M., Champin, P.-A., Georgeon, O.L., Mille, A.: Trace-based reasoning - modeling interaction traces for reasoning on experiences. In: Boonthum-Denecke, C., Youngblood, G.M. (eds.) FLAIRS Conference. AAAI Press (2013)

    Google Scholar 

  10. Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)

    Google Scholar 

  11. Zarka, R., Cordier, A., Egyed-Zsigmond, E., Mille, A.: Contextual trace-based video recommendations. In: Mille, A., Gandon, F.L., Misselis, J., Rabinovich, M., Staab, S. (eds.) WWW (Companion Volume), pp. 751–754. ACM (2012)

    Google Scholar 

  12. Doumat, R., Egyed-Zsigmond, E., Pinon, J.-M.: User trace-based recommendation system for a digital archive. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 360–374. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. Esslimani, I., Brun, A., Boyer, A.: From social networks to behavioral networks in recommender systems. In: Memon, N., Alhajj, R. (eds.) ASONAM, pp. 143–148. IEEE Computer Society (2009)

    Google Scholar 

  14. Liao, Z.-X., Pan, Y.-C., Peng, W.-C., Lei, P.-R.: On mining mobile apps usage behavior for predicting apps usage in smartphones. In: He, Q., Iyengar, A., Nejdl, W., Pei, J., Rastogi, R. (eds.) CIKM, pp. 609–618. ACM (2013)

    Google Scholar 

  15. Liao, Z.-X., Peng, W.-C., Yu, P.S.: Mining usage traces of mobile apps for dynamic preference prediction. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 339–353. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  16. Nepal, S., Paris, C., Bista, S.K.: Srec: a social behaviour based recommender for online communities. In: Herder, E., Yacef, K., Chen, L., Weibelzahl, S. (eds.) UMAP Workshops. CEUR Workshop Proceedings, vol. 872. CEUR-WS.org (2012)

    Google Scholar 

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

O’Shea, E., Delany, S.J., Lane, R., Namee, B.M. (2014). NudgeAlong: A Case Based Approach to Changing User Behaviour. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11209-1_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11208-4

  • Online ISBN: 978-3-319-11209-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics