Difference between revisions of "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 | + | 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 [http://glaros.dtc.umn.edu/gkhome/slim/overview?q=suggest/overview 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 | + | The SLIM library can be downloaded from [http://www-users.cs.umn.edu/~xning/slim/html/ here]. |
Revision as of 17:05, 3 April 2013
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
