in conjunction with the DePaul Center for


Event Sponsors:

in cooperation with SIGECOM, SIGKDD, and SIGCHI

Welcome to ACM Recommender Systems 2011

We are pleased to invite you to participate in this premier annual event on research and applications of recommender technologies.

The 5th ACM International Conference on Recommender Systems builds on the success of the Recommenders 06 Summer School in Bilbao, Spain and the series of four successful conference events from 2007 to 2010 in Minneapolis (2007), Lausanne (2008), New York (2009) and Barcelona (2010).

In these events many members of the practitioner and research communities valued the rich exchange of ideas made possible by the shared plenary sessions. The 5th International conference will promote the same close interaction among practitioners and researchers. Published papers will go through a full peer review process. The conference proceedings, which are available both as bound volume and via the ACM Digital Library, are expected to be widely read and cited.

In addition to a regular technical program, there will be several tutorials covering the state-of-the-art of this domain, different workshops, a doctoral consortium, and an industrial program comprising of panel discussions and a practice/industry-paper track.  We also expect to have keynote speakers who will address the major applications of recommendation technologies in industry.

Conference: October 23-27, 2011

Bamshad Mobasher, General Chair, DePaul University, USA
Robin Burke, General Chair, DePaul University, USA


Gediminas Adomavicius, Program Co-Chair, University of Minnesota, USA
Dietmar Jannach, Program Co-Chair, TU Dortmund, Germany


About Recommender Systems

Recommender systems are software applications that aim to support users in their decision-making while interacting with large information spaces. They recommend items of interest to users based on preferences they have expressed, either explicitly or implicitly. The ever-expanding volume and increasing complexity of information on the Web has therefore made such systems essential tools for users in a variety of information seeking or e-commerce activities. Recommender systems help overcome the information overload problem by exposing users to the most interesting items, and by offering novelty, surprise, and relevance. Recommender technology is hence the central piece of the information seeking puzzle. Major e-commerce sites such as Amazon and Yahoo are using recommendation technology in ubiquitous ways. Many new comers are on their way and entrepreneurs are competing in order to find the right approach to use this technology effectively.

Recent books and more information about recommender systems can be found here: