CfP: 2nd Large-Scale Recommender Systems and RecSysTV

Danny Bickson just sent me the following:

Hi Igor,

Following our previous conversation both our workshops were accepted to be part of ACM Recsys 2014, here are some additional details:

• http://graphlab.com/community/events/lsrs14.html - focused large scale recommender systems
• http://boxfish.com/recsys - focused on TV recommendations

The workshops will take place as part of ACM Recsys 2014, June 6-10 at the bay area.

Submission date for both workshops is July 21st, 2014.

We would love if you could blog about it in your blog - as matrix factorization methods is the #1 silver bullet for collaborative filtering solutions.. :-)

Sure Danny, I am glad some people are disrupting recommender systems and TV!

From the first website:

2nd Large-Scale Recommender Systems: Research and Best Practice 

As we enter the era of Big Data, the modern Recommender System faces greatly increased data volume and complexities. Previous computational models and experience on small data may not hold today, thus, how to build an efficient and robust system has become an important issue for many practitioners. Meanwhile, there is an increasing gap between academia research of recommendation systems focusing on complex models, and industry practice focusing on solving problems at large scale using relatively simple techniques.

Chances favor connected minds. The motivation of this workshop is to bring together researchers and practitioners working on large-scale recommender system in order to: (1) share experience, techniques and methodologies used to develop effective large-scale recommender, from architecture, algorithm, programming model, to evaluation (2) identify key challenges and promising trends in the area, and (3) identify collaboration opportunities among participants.

We invite industrial level recommendation system practitioners to submit extended abstracts (1-4 pages), or slides (~20 pages). We also invite recommendation systems researchers to submit extended abstract (1-4 pages) on their new research related to system aspect of recommendation with Big Data.

Our topics of interests include, but are not limited to:

Systems of Large-scale RS:

• Architecture

• Programming Model

• Distributed systems

• Real-time recommendation

• Online learning for recommendation

• Scalability and Robustness

Data & Algorithms in Large-scale RS:

• Big data processing in offline/near-line/online modules

• Streaming data for recommendation

• Data platforms for recommendation

• Large, unstructured and social data for recommendation

• Heterogeneous data fusion

• Sampling techniques

• Parallel algorithms

• Incremental algorithms

• Algorithm validation and correctness checking

Application & Evaluation of Large-scale RS:

• Emerging applications

• Explanations in Large-scale RS

• Anti-attack of Large-scale RS

• Large data and privacy issue

• Evaluation methodology

• Large user studies

• Measurement platforms

• Visualization

Organizing Committee

• Tao Ye, Pandora Internet Radio

• Qiang Yan, Taobao

• Danny Bickson, GraphLab Inc.

Program committee

• Jan Neumann, Comcast

• Sebastian Schelter, TU Berlin

• Sean Owen, Cloudera

• Royi Ronen, Microsoft

• Nikolas Vasiloglou, Ismion

• Mohan Reddy, Zynga

• Noam Koenigstein, Microsoft

• Demian Bellumio, Senzari

• Tal Kedar, Sears

• Assaf Araki, Intel

• Quan Yuan, Taobao

• Baldo Faieta, Adobe

• Nicholas Jing Yuan, Microsoft Research

• Tao Zhou, UESTC

and from the second webpage:

We are pleased to invite you to participate in the First Workshop on Recommendation Systems for Television and online Video (RecSysTV) that is happening in conjunction with the ACM RecSys 2014 conference in Foster City, 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 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

• 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)

• 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 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.


Important dates:
7/21/2014: Paper submission deadline
8/21/2014: Notification to authors
9/5/2014: 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 double-column ACM SIG format. Additional information about formatting and style files are 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 to the CMT Web site by 11:59pm Pacific Time on Monday July 21st 2014.


Organizing committee
Danny Bickson, Graphlab Inc., Seattle, WA
John Hannon, Boxfish, Palo Alto, CA
Jan Neumann, Comcast Labs, Washington, DC
Hassan Sayyadi, Comcast Labs, Washington, DC
Program committee:
Justin Basilico, Netflix
Hidasi Balazs, GravityR&D
Craig Carmichael, Rovi
Emanuele Coviello, Keevio
Pádraig Cunningham, Insight Centre for Data Analytics
Ben Jordan, Rovi
Noam Koenigstein, Microsoft
Gert Lanckriet, UC San Diego
Hayim Makabee, Yahoo! Labs
Royi Ronen, Microsoft
Barry Smyth, Insight Centre for Data Analytics
Domonkos Tikk, GravityR&D
Ari Tuchman, quantiFind
Udi Weinsberg, Technicolor Labs
Esti Widder, Viaccess-Orca
Ho-Hsiang Wu, Rd.io
Dávid Zibriczky, GravityR&D

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