Predicting Future User Behaviour in Interactive Live TV
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.
Keywordsrecommender system interactive live TV multistream
Unable to display preview. Download preview PDF.
- 1.Amazon.com: Site features:recommendations (2007)Google Scholar
- 2.Schachter, J.: del.icio.us: people who like recommendations also likes. (2005)Google Scholar
- 3.last.fm (2007), http://last.fm
- 5.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)CrossRefGoogle Scholar
- 7.Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering (2001)Google Scholar
- 12.LIVE: Live staging of media events (2007), http://www.ist-live.org
- 13.MECiTV: Media collaboration for interactive tv (2004), http://www.meci.tv