Personalized and Context-Aware TV Program Recommendations Based on Implicit Feedback

  • Paolo Cremonesi
  • Primo Modica
  • Roberto Pagano
  • Emanuele RabosioEmail author
  • Letizia Tanca
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 239)


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.


Implicit Feedback Leverage Context Information Personalized Video Recorder (PVRs) Initial User Profile Familiar Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Paolo Cremonesi
    • 1
  • Primo Modica
    • 1
  • Roberto Pagano
    • 1
  • Emanuele Rabosio
    • 1
    Email author
  • Letizia Tanca
    • 1
  1. 1.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanoItaly

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