Recommender 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). We often have heard the term “so many choices, so little to watch” which expresses the desire for recommendation systems to help consumers deal with the often overwhelming choices they face.

TV and online video recommendation systems face a number of unique challenges, for example, the content available on TV is constantly changing and often only available once which leads to severe cold start problems and we consume our entertainment in groups of varying compositions (household vs individual) which makes building taste profiles and modeling consumer behavior very challenging, Recommendation systems also have to address a number of very different consumption patterns, such as actively browsing through a list of personalized Video on Demand choices that match our current mood, compared to enjoying a “lean back experience” where a recommendation systems playlists a stream of TV shows from our favorite channels for us.

  • Danny Bickson, Graphlab Inc., Seattle, WA
  • John Hannon, Boxfish, Palo Alto, CA
  • Jan Neumann, Comcast Labs, Washington, DC
  • Hassan Sayyadi, Comcast Labs, Washington, DC
Workshop Date

Oct 10, 9.00 – 17.30


Alexandria Room

Web site

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