Abstract
The current explosion of the number of available channels is making the choice of the program to watch an experience more and more difficult for TV viewers. Such a huge amount obliges the users to spend a lot of time in consulting TV guides and reading synopsis, with a heavy risk of even missing what really would have interested them. In this paper we confront this problem by developing a recommender system for TV programs. Recommender systems have been widely studied in the video-on-demand field, but the TV domain poses its own challenges which make the traditional video-on-demand techniques not suitable. In more detail, we propose recommendation algorithms relying exclusively on implicit feedback and leveraging context information. An extensive evaluation on a real TV dataset proves the effectiveness of our approach, and in particular the importance of the context in providing TV program recommendations.
This research is partially supported by the IT2Rail project funded by European Union’s Horizon 2020 research and innovation program under grant agreement No: 636078, and by the Italian project SHELL CTN01_00128_111357 of the program “Cluster Tecnologici Nazionali”.
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Cremonesi, P., Modica, P., Pagano, R., Rabosio, E., Tanca, L. (2015). Personalized and Context-Aware TV Program Recommendations Based on Implicit Feedback. In: Stuckenschmidt, H., Jannach, D. (eds) E-Commerce and Web Technologies. EC-Web 2015. Lecture Notes in Business Information Processing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-27729-5_5
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DOI: https://doi.org/10.1007/978-3-319-27729-5_5
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