2nd Workshop on Recommendation Systems for Television and Online Video

For many households the television is still the central entertainment hub in their home, and the average TV viewer spends about half of their leisure time in front of a TV (3-5 hours/day). The choice of what to watch becomes more overwhelming though because the entertainment options are scattered across various channels, such as on-demand video, digital recorders (on premise or in the cloud) and the traditional linear TV. In addition, consumers can also access the content not just on the big screen, but also on their computers, phones, and tablet devices. Recommendation systems provide TV users with suggestions about both online video-on-demand and broadcast content and help them to search and browse intelligently for content that is relevant to them. While many open questions in video-on-demand recommendations have already been solved, recommendation systems for broadcast content (e.g., linear channels and catch-up TV) still experience a number of unique challenges due to the peculiarity of such domain. For example, the content available on linear channels is constantly changing and often only available once which leads to severe cold start problems and we often consume TV in groups of varying compositions (household vs individual) which makes building taste profiles and modeling consumer behavior very challenging.

Organizers
  • , Comcast Labs, USA
  • , Boxfish, USA
  • , ContentWise R&D, Italy
  • , Dato Inc., USA
  • , Comcast Labs, USA
Website

http://recsys.tv/

Date

Saturday, Sept 19, 2015, 09:00-17:30

Location

HS 7

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