SUGGEST: Recommendation Engine

Current version: 1.0, 11/15/2000

SUGGEST is a Top-N recommendation engine that implements a variety of recommendation algorithms. Top-N recommender systems, a personalized information filtering technology, are used to identify a set of N items that will be of interest to a certain user. In recent years, top-N recommender systems have been used in a number of different applications such to recommend products a customer will most likely buy; recommend movies, TV programs, or music a user will find enjoyable; identify web-pages that will be of interest; or even suggest alternate ways of searching for information.

The algorithms implemented by SUGGEST are based on collaborative filtering that is the most successful and widely used framework for building recommender systems. SUGGEST implements two classes of collaborative filtering-based top-N recommendation algorithms, called user-based and item-based.


Provides high quality recommendations!
On a wide range of datasets, the item-based recommendation algorithms produce results whose quality is up to 30% better than that achieved of traditional collaborative filtering-based algorithms.
Achieves low recommendation latency!
The item-based algorithms can compute top-10 recommendations in less that 5us on modern workstations and servers.
Scales to large datasets!
Both the user-based and item-based algorithms can scale to very large datasets, without significant degradation in performance.