RecSysTV 2014 : First Workshop on Recommender Systems for Television and Online Video (RecSysTV) 2014
City: Foster City
Abstr. due: 21.07.2014
Dates: 06.10.14 — 10.10.14
Organizing comittee e-mail: firstname.lastname@example.org
Organizers: Graphlab Inc.
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 recommender systems to help consumers deal with the often overwhelming choices they face.
TV and online video recommender 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, Recommender 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 recommender system 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 or 20 slides) 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
- How can social media improve TV recommendations
- Cross-domain recommendation algorithms (linear TV, video on demand, DVR, gaming consoles)
- Multi-viewer profile separation
- Evaluation metrics for TV and online video recommendation
- 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
- Video playlisting
- Linear TV usage and box office success prediction
- Personalized advertisement recommendations
- Recommendations of 2nd screen web content
- Recommendations of short form videos (previews, trailers, music videos)
Conference Web-Site: http://boxfish.com/recsys