Abstract
Previous personalized DTV recommendation systems focus only on viewers’ historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information—AIMED. The AIMED data is fed into a neural network model to predict TV viewers’ program preferences. Evaluation results indicate that the AIMED model significantly increases recommendation accuracy and decreases prediction errors compared to the conventional model.
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
Alspector, J., Kolez, A., Karunanithi, N.: Comparing Feature-Based and Clique-Based User Models for Movie Selection. In: Proceedings of the Third ACM Conference on Digital Libraries, Pittsburgh PA, ACM Press, New York (1998)
Ang, I.: Living Room Wars. Routledge, London (1996)
Ansari, A., Essegaier, S., Kohli, R.: Internet recommendation systems. Journal of Marketing Research 37(3), 363–375 (2000)
Ardissono, L., et al.: User modeling and recommendation techniques for personalized electronic program guides. In: Ardissono, L., Kobsa, A., Maybury, M. (eds.) Personalized Digital Television: Targeting Programs to Individual Viewers, pp. 3–26. Kluwer Academic Publishers, Dordrecht (2004)
Balabanovic, M., Shoham, Y.: FAB: Content-Based Collaborative Recommender. Communications of the ACM 40(3), 66–72 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and content-based information in recommendation. In: Recommender System Workshop’ 98, pp. 11–15 (1998)
Baudisch, P., Brueckner, L.: TV Scout: Guiding Users from Printed TV Program Guides to Personalized TV Recommendation. In: Proceedings of the 2nd Workshop on Personalization in Future TV, Malaga, Spain, pp. 157–166 (2002)
Burke, R.: Hybrid Recommender Systems: Survey and Experiments. User Modeling and User Adapted Interaction 12(4), 331–370 (2002)
Frank, R., Greenberg, M.: The public’s use of television: Who watches what and why. Sage, Beverly Hills (1980)
Gena, C., Ardissono, L.: On the Construction of TV Viewer Stereotypes Starting from Lifestyle Surveys. In: Workshop on Personalization in Future TV, Sonthofen, Germany (2001)
Hsu, H.S., Wen, M.H., Lee, C.H.: An Activity-Oriented Approach to Designing a User Interface for Digital Television. In: Proceedings of the 4th Euro iTV conference, Athens, Greece, pp. 83–90 (2006)
Perse, E.M.: Implications of cognitive and affective involvement for channel changing. Journal of Communication 48(3), 49–68 (1998)
Reynolds, F.D., Darden, W.R.: Constructing life style and psychographics. In: Wells, W.D. (ed.) Life style and Psychographics, Chicago: pp. 71–96. American Marketing Association, Chicago (1974)
Wang, J., et al.: Personalization of a peer-to-peer television system. In: Proceedings of the 4th EuroiTV conference, Athens, Greece, pp. 147–155 (2006)
Zillmann, D., Hezel, R.T., Medoff, N.J.: The effect of affective states on selective exposure to televised entertainment fare. Journal of Applied Social Psychology 10, 323–339 (1980)
Zimmerman, J., et al.: TV Personalization system. Design of a TV Show Recommender Engine and Interface. In: Ardissono, L., Kobsa, A., Maybury, M. (eds.) Personalized Digital Television: Targeting Programs to Individual Viewers, pp. 27–51. Kluwer Academic Publishers, Dordrecht (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Hsu, S.H., Wen, MH., Lin, HC., Lee, CC., Lee, CH. (2007). AIMED- A Personalized TV Recommendation System. In: Cesar, P., Chorianopoulos, K., Jensen, J.F. (eds) Interactive TV: a Shared Experience. EuroITV 2007. Lecture Notes in Computer Science, vol 4471. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72559-6_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-72559-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72558-9
Online ISBN: 978-3-540-72559-6
eBook Packages: Computer ScienceComputer Science (R0)