Software

We announce the release of SUGGEST (Ver. 1.0), a Top-N
recommender engine based on collaborative filtering. SUGGEST implements
a variety of user- and item-based recommendation algorithms that can
be used to compute personalized item recommendations.
The key feature of SUGGEST is a set of new model-based recommendation
algorithms that were shown to produce high quality recommendations in
a few microseconds.

Here is a list of some of SUGGEST's features:

  - It is provided in a library form so it can be directly
    included in user's applications.

  - It was designed for high performance and scalability.

  - It has low model building computational requirements.

  - It has very high Top-N recommendation rates.
    Over 20,000 recommendations/second on Pentium III class
    workstations.

  - It has small memory foot-print.

Obtaining SUGGEST
-----------------

SUGGEST is freely distributed. Information on how to get the package
is available on WWW at:

  http://www.cs.umn.edu/~karypis/suggest

SUGGEST has been written by George Karypis, at the Computer Science
Department of the University of Minnesota. If you have any questions or
problems obtaining SUGGEST, send email to karypis@cs.umn.edu.

SUGGEST is copyrighted by the Regents of the University of Minnesota.