ACM RecSys 2010 Workshop on the  


The PRSAT 2010 proceedings are now available in the CEUR series.


User modeling, adaptation, and personalization techniques have hit the mainstream. The explosion of social network websites, on-line user-generated content platforms, and the tremendous growth in computational power of mobile devices are generating incredibly large amounts of user data, and an increasing desire of users to "personalize" (their desktop, e-mail, news site, phone).
The potential value of personalization has become clear both as a commodity for the benefit or enjoyment of end-users, and as an enabler of new or better services –a strategic opportunity to enhance and expand businesses.
An exciting characteristic of recommender systems is that they draw the interest of industry and businesses while posing very interesting research and scientific challenges.
In spite of significant progress in the research community, and industry efforts to bring the benefits of new techniques to end-users, there are still important gaps that make personalization and adaptation difficult for users. Research activities still often focus on narrow problems, such as incremental accuracy improvements of current techniques, sometimes with ideal hypotheses, or tend to overspecialize on a few applicative problems (typically TV or movie recommenders –sometimes simply because of the availability of data). This restrains de facto the range of other applications where personalization technologies might be useful as well.
Thus, we may have reached a good point to take a step back to seek perspective in the research done in recommender systems. This workshop contrives for a new uptake on past experiences and lessons learned. We propose an analytic outlook on new research directions, or ones that still require substantial research, with a special focus on their practical adoption in working applications, and the barriers to be met in this path.
This workshop aims at bringing the gap between academic researchers and industry practitioners in the area of Recommender Systems. We are interested both in research work that faces real industry problems, and in industry cases that create research challenges.
The workshop will favor position papers, and innovative approaches with original or compelling concepts that stimulate discussions rather than the papers with incremental contributions, even if fully evaluated.


Topics of interests

This workshop is an opportunity to bring together researchers and practitioners to discuss, on one hand, the main lessons drawn from successes but also from failures of recommender systems, and on the other hand, identify and analyze the major research areas in recommendation and personalization technologies that should be addressed in the future for a practical, effective take-up of the needs of vendors, consumers, and technology providers. Thus, topics of interest include, but are not limited to:

  • Limits of recommender systems
    • main bottlenecks, research dead ends and myths in recommender systems
    • missing technology pieces for wider adoption
    • social (privacy, culture) issues
  • Analytical view of personalization experiences
    • case studies of recommender system implementations & deployments
    • evaluation and user studies of recommender systems
    • scalability in large recommender systems
    • lessons learnt from your past experience
    • obstacles to massive deployment of recommendation solutions in industrial environments
  • Recommender systems in broader systems
    • place of recommender systems in complete systems
    • killer application area
  • Next needs in recommender systems
    • new business models related to recommendation
    • social and cultural impact of recommender systems
    • new paradigms to provide recommendations
    • new areas for recommendations
    • users' expectations about future recommender systems
    • beyond one-shot recommendations: recommendations of sequences, goal-oriented recommendations, ...



The format of the workshop will combine short paper presentations with open discussion sessions. Papers will be grouped by topics allowing us to organize an open discussion for every topic. Active participation will be expected and formal conference-style presentations will be minimal. We invite two kinds of submissions:

  • Short papers / position papers reporting on lessons learnt, concrete problems, new promising areas. The maximum length is 4 pages in the standard ACM SIG proceedings format.
  • Long papers. The maximum length is 8 pages in the standard ACM SIG proceedings format.

Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop. Each paper will be reviewed by at least 3 independent referees.

Papers should be submitted in PDF format. Submissions should be prepared according to the standard ACM SIG proceedings format. All submissions will be done electronically via the PRSAT 2010 Web submission system:

At least one author of each accepted paper must register for the workshop. Information about registration will appear soon on the RecSys 2010 Web page:


Important dates
  • Paper submission deadline: July 7, 2010
  • Notification of acceptance/rejection: July 29, 2010
  • Camera-ready version due: August 16, 2010
  • Workshop: September 30, 2010


Organizing committee


Program committee
  • David Bonnefoy, Pearltrees
  • Makram Bouzid, Alcatel-Lucent Bell Labs
  • Iván Cantador, Universidad Autónoma de Madrid
  • José Carlos Cortizo, Universidad Europea de Madrid & BrainSins
  • Alexander Felfernig, Graz University of Technology & ConfigWorks
  • Ido Guy, IBM Haifa Research Lab
  • Paola Hobson, Snell
  • Rubén Lara, Telefónica I+D
  • Kevin Mercer, BBC
  • Andreas Nauerz, IBM Deutschland Research & Development GmbH
  • Michael Papish, Media Unbound, Inc.
  • Igor Perisic, LinkedIn Corporation
  • Myriam Ribière, Alcatel-Lucent Bell Labs
  • Neel Sundaresan, eBay Research Labs
  • Marc Torrens, Strands, Inc.
  • Andreas Töscher, Commendo Research & Consulting GmbH
  • Xiaohui Xue, SAP

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