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 [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. | + | '''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 [http://www-users.cs.umn.edu/~xning/slim/html/ here]. | The SLIM library can be downloaded from [http://www-users.cs.umn.edu/~xning/slim/html/ here]. | ||
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== Literature == | == Literature == | ||
| − | [http://dl.acm.org/ft_gateway.cfm?id=2365983&ftid=1284755&dwn=1&CFID=291662235&CFTOKEN=92204067 SLIM: Sparse Linear Methods for Top-N Recommender Systems, Xia Ning and George Karypis, ICDM , 2011] | + | * [http://dl.acm.org/ft_gateway.cfm?id=2365983&ftid=1284755&dwn=1&CFID=291662235&CFTOKEN=92204067 SLIM: Sparse Linear Methods for Top-N Recommender Systems, Xia Ning and George Karypis, ICDM, 2011] |
== External links == | == External links == | ||
Revision as of 06:09, 4 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.
