Workshop on Large-Scale Recommender Systems and the Netflix Prize
Competition
http://netflixkddworkshop2008.info/

Held in conjunction with
The 13th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD 2008)

August 24-27, 2008, Las Vegas, NV

Workshop Description

Recommender systems have emerged over the last several years as an
important area of research spanning the boundaries of such diverse set of
disciplines as data mining, machine learning, information retrieval,
human-computer interaction, marketing and operations research. Interest in
recommender systems was further enhanced when Netflix announced its
$1,000,000 prize competition in October 2006 that attracted over 20,000
participants from 167 different countries. One of the sub-fields of
recommender systems that benefited very significantly from the Netflix
Prize competition is the area of large-scale recommender systems, which
deals with scaling recommendation methods to large datasets. Many Netflix
competitors came to realize that some of the well-known recommendation
algorithms would not scale well to the Netflix dataset. In addition, some
of the most popular and well-regarded methods would perform poorly on the
Netflix dataset . maybe because the asymptotic performance of these
methods is quite different from their performance on smaller datasets.

Workshop Topics

This workshop will address these scalability and performance issues by
focusing on recommendation methods explicitly designed to handle large
data sets. The topics of interest include (but are not limited to):

* Novel recommendation models, emphasizing accuracy, performance and
asymptotic behavior
* Scalability problems in recommender systems
* Novel evaluation methodologies for recommendation quality
* Efficient integration of multiple complementary predictors
* Studies of content-filtering vs. collaborative filtering and their
integration in large-scale environments
* Explaining and presenting recommendations to end-users
* Idiosyncrasies of the Netflix Prize Dataset and lessons learned from
its analysis
* Netflix Prize competition at large

Paper Submission

We invite the submission of papers on these and related topics by
researchers in the recommender systems field as well as the participants
of the Netflix Prize competition. All submitted papers will be evaluated
by the workshop program committee based on scientific merits and novelty
as perceived by the committee. Accepted papers will appear in the workshop
proceedings. At least one author of each accepted paper is expected to
register for the workshop and present the paper.

The papers may be submitted either as full or short papers. The page limit
for a full paper is 8 pages and for a short paper is 4 pages inclusive of
all references and figures. All submitted papers must be in the PDF format
and use standard templates that can be found here.

Please submit your manuscript in PDF format at the paper submission
website.

Important Dates

* May 30, 2008: Electronic submission of full papers & abstracts
* June 27, 2008: Author notification
* July 7, 2008: Submission of Camera-ready papers
* August 24, 2008: Workshop in Las Vegas, California

Workshop Co-Chairs

* Alex Tuzhilin (chair), NYU Stern . E-mail: atuzhili @ stern.nyu.edu
* Yehuda Koren (co-chair), AT&T Labs--Research. E-mail: yehuda @
research.att.com
* Jim Bennett, Netflix. E-mail: jbennett @ netflix.com
* Charles Elkan, University of California, San Diego. E-mail: elkan @
cs.ucsd.edu
* Daniel Lemire, University of Quebec at Montreal (UQAM). E-mail:
lemire @ acm.org

Disclaimer: To avoid conflict of interest, participants in the Netflix
Prize competition will not handle submitted papers, and will not be
involved in the paper selection and reviewing process.

--
Sean M. McNee, Ph.D.
Nil Desperandum.