Abstract
Recommender systems are a means of personalisation providing their users with personalised recommendations of items that would possibly suit the users needs. They are used in a broad area of contexts where items are somehow linked to users. The creation of recommendations of interactive live TV suffers from several inherent problems, e.g. the impossibility to foresee the contents of the next items or the reactions of the user to the changing programme.
This paper proposes an algorithm for building personalised streams within interactive live TV. The development of the algorithm comprises a basic model for users and media items. A first preliminary evaluation of the alogithm is executed and the results discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Amazon.com: Site features:recommendations (2007)
Schachter, J.: del.icio.us: people who like recommendations also likes. (2005)
last.fm (2007), http://last.fm
Reddy, S., Mascia, J.: Lifetrak: music in tune with your life. In: Proceedings of the 1st ACM international Workshop on Human-Centered Multimedia, HCM 2006, Santa Barbara, California, USA, October 27-27, 2006, pp. 25–34. ACM Press, New York (2006)
Cosley, D., Lam, S.K., Albert, I., Konstan, J.A., Riedl, J.: Is seeing believing?: how recommender system interfaces affect users’ opinions. In: CHI 2003 Extended Abstracts on Human Factors in Computing Systems, Ft. Lauderdale, Florida, USA, April 05-10, 2003, pp. 585–592. ACM Press, New York (2003)
O’Donovan, J., Smyth, B.: Trust in recommender systems. In: IUI 2005: Proceedings of the 10th international conference on Intelligent user interfaces, San Diego, California, USA, pp. 167–174. ACM Press, New York (2005)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering (2001)
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)
Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In: Proceedings of the 16th international Conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, May 08-12, 2007, pp. 211–220. ACM Press, New York (2007)
Marlow, C., Naaman, M., Boyd, D., Davis, M.: Ht06, tagging paper, taxonomy, flickr, academic article, to read. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, HYPERTEXT 2006, Odense, Denmark, August 22-25, 2006, pp. 31–40. ACM Press, New York (2006)
Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T.: The vocabulary problem in human-system communication. Communications of the ACM 30(11), 964–971 (1987)
LIVE: Live staging of media events (2007), http://www.ist-live.org
MECiTV: Media collaboration for interactive tv (2004), http://www.meci.tv
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gude, M., Grünvogel, S.M., Pütz, A. (2008). Predicting Future User Behaviour in Interactive Live TV. In: Tscheligi, M., Obrist, M., Lugmayr, A. (eds) Changing Television Environments. EuroITV 2008. Lecture Notes in Computer Science, vol 5066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69478-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-69478-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69477-9
Online ISBN: 978-3-540-69478-6
eBook Packages: Computer ScienceComputer Science (R0)