RecSysTV 2014 - First Workshop on Recommender Systems for Television and Online Video (RecSysTV) 2014

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 often consume our entertainment in groups of varying compositions (household vs individual) which makes building taste profiles and modeling consumer behavior very challenging, Finally, recommendation systems 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.
We believe that this workshop is of great interest to both academic researchers and industrial practitioners due to the importance of TV and online video in our daily lives and the challenging technical problems that need to be addressed.
We invite both long papers (up to 8 pages) that present original mature research and short papers (up to 4 pages) that describe early/promising research, demos or industrial case studies focusing on (but are not limited to):
Context-aware TV and online video recommendations
Leveraging contextual viewing behaviour, e.g. device specific recommendations
Mood based recommendations
Group recommendations
User modeling & leveraging user viewing and interaction behavior
How can social media improve TV recommendations
Cross-domain recommendation algorithms (linear TV, video on demand, DVR, gaming consoles
Multi-viewer profile separatio
Evaluation metrics for TV and online video recommendation
Content-based TV and online video recommendations
Analysis techniques for video recommendations based on video, audio, or closed caption signals
Utilization of external data sources (movie reviews, ratings, plot summaries) for recommendations
Dataset & API Access:
For the duration of the CFP for RecSysTV the Boxfish API will be made available to those who wish to use it. To access the Boxfish API you must:
1) Email organizers at recsystv.org to request the Promo Code
2) Go to http://boxfish.com/get-started and enter details and Promo Code
Extra undocumented endpoints will potentially be made available to RecSysTV participants. These endpoints will be communicated via the email used to register.