7.5.10

CFP: International Workshop on the Practical Use of Recommender Systems, Algorithms and Technologies (PRSAT 2010)

International Workshop on the Practical Use of Recommender Systems, Algorithms and Technologies (PRSAT 2010)
30 September 2010 | Barcelona, Spain
In conjunction with the 4th ACM Conference on Recommender Systems (RecSys 2010)

Dates

  • Paper submission: July 1, 2010
  • Notification of acceptance/rejection: July 26, 2010
  • Camera-ready copies of accepted papers: August 16, 2010
  • Workshop: September 30, 2010

Motivation

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.

Topics of interest

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
  • Recommendation 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, ...