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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Fogg, B.: A behavior model for persuasive design. In: Proceedings of the 4th International Conference on Persuasive Technology, p. 40. ACM (2009)
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)
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)
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)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artificial Intellegence 2009 (2009)
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)
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)
Palanivel, K., Sivakumar, R.: A study on collaborative recommender system using fuzzy-multicriteria approaches. IJBIS 7(4), 419–439 (2011)
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)
Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)