RecSys’09: Third ACM

Conference on Recommender Systems

New York City, NY, USA • October 22-25, 2009

Paper Submission: May 8, 2009

ORGANIZING COMMITTEE

General Chairs:

Lawrence Bergman, IBM Research, USA Alex Tuzhilin, New York University, USA

Program Chairs:

Robin Burke, DePaul University, USA Alexander Felfernig,

Graz University of Technology, Austria Lars Schmidt-Thieme,

University of Hildesheim, Germany

Industry Chairs:

John Ciancutti, Netflix, USA

Paul Lamere, Sun Microsystems, USA

Workshop Chairs:

Joseph Konstan, University of Minnesota, USA Sean McNee, FTI Technology, USA

Doctoral Symposium Chairs:

Michael O’Mahoney, University College Dublin, Ireland Paul Resnick, University of Michigan, USA

Publicity Chair:

Zan Huang, Pennsylvania State University, USA

Asian Liaison:

Dong Zhang, Google Research, China

European Liaison:

Alexandros Nanopoulos,

University of Hildesheim, Germany

RecSys’09: The Third ACM Conference on Recommender Systems builds on

the success of Recommenders 06 Summer School in Bilbao, Spain, RecSys’07

in Minneapolis, USA, and RecSys’08 in Lausanne, Switzerland. Many members

of the practitioner and research communities valued the rich exchange of ideas

made possible by the shared plenary sessions at these events. RecSys’09

will promote the same close interaction among practitioners and researchers,

reaching a wider range of participants including those from Europe and Asia.

Published papers will go through a full peer review process. The conference

proceedings are expected to be widely read and cited. In addition to a regular

technical program, there will be tutorials covering the state-of-the-art of this do-

main, a doctoral consortium, an industrial program comprised of keynote speak-

ers and practice/industry-paper tracks, and special-topic workshops.

TOPICS OF INTEREST INCLUDE (but are not limited to):

• Case studies of recommender system • Recommendation algorithms

implementations • Recommendation in social networks

• Conversational recommender systems • Recommender system interfaces

• Context-aware and multidimensional rec- • Scalability issues

ommender systems • Security and privacy

• Evaluation of recommender systems • Semantic web technologies for

• Group recommenders recommender systems

• The impact of recommenders in practice • Theoretical aspects of recommender

• Innovative recommender applications systems

• Novel paradigms of recommender systems • User modeling and recommender systems

• Personalization • Userstudies

More information at http://recsys.acm.org