SLIM
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SLIM is a library that implements a set of top-N recommendation methods based on sparse linear models. These models are a generalization to the traditional item-based nearest neighbor collaborative filtering approaches implemented in SUGGEST, and use the historical information to learn a sparse similarity matrix by combining an L2 and L1 regularization approach.
The SLIM library can be downloaded from here.
Literature
SLIM: Sparse Linear Methods for Top-N Recommender Systems, Xia Ning and George Karypis, ICDM , 2011
