SUGGEST is a Top-N recommendation engine that implements a variety of recommendation algorithms for collaborative filtering.

Python wrapper by Ricardo Niederberger Cabral (ricardo.cabral at imgseek.net).

Recommendation engine by George Karypis (http://glaros.dtc.umn.edu/gkhome/suggest/overview).

More about the wrapped library (SUGGEST):

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.

Building

Can be built on Windows and Unix compatible systems with

python setup.py build

Visual Studio 2003 is needed for building on windows. Linux (Ubuntu 32bits) shared library is provided.

FAQ

Segmentation faults when initializing the prediction model

Due to a SUGGEST undocumented detail, you must feed item ids into the item[] array in ascending order.