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


When Oct 6, 2014 - Oct 10, 2014
Where Foster City, CA, USA
Submission Deadline Jul 21, 2014
Notification Due Aug 21, 2014
Final Version Due Sep 5, 2014
Categories    recommendation systems   machine learning   data mining   big data

Call For Papers

1st Workshop on Recommender Systems for Television and Online Video

We are pleased to invite you to participate in the 1st Workshop on Recommender Systems
for Television and Online Video (RecSysTV) that is happening in conjunction with the ACM
RecSys 2014 conference in Foster City, CA (Silicon Valley), USA from 6th-10th October 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 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.

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

** 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 separation
- Evaluation metrics for TV and online video recommendations

** 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

** Other topics related to TV and online video 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)

Accepted long-papers will be presented in a plenary oral session or together with the
accepted short papers in a poster session which can include a systems demonstration.

Important dates:

07/21/2014: Paper submission deadline
08/21/2014: Notification to authors
09/05/2014: Camera-ready version due

Paper format and submission:

The submission requirements for this workshop are in line with standard RecSys formatting
guidelines. We request potential submitters to adhere to the double-column ACM SIG
format. Additional information about formatting and style files is available online
(tighter alternate style). The review process is single-blind, not double-blind (i.e. not
anonymized), thus, please include the author's names.

Papers must be electronically submitted via the workshop submission website by 11:59pm Pacific Time on Monday July
21st 2014.

Organizing committee (Email: organizers at

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

Boxfish Data Challenge:

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 to request the Promo Code
2) Go to 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.

For up to date information about the workshop, please see the workshop website at

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