I believe there is already a recommender framework in the scikits family already called crab? http://muricoca.github.io/crab/
Few days back, one of the committers to sklearn spoke about the fact that he detected code in crab that looked like his own. Given that there is so much reuse, would it make sense for crab (aka scikits.recommender) to use sklearn as a dependency and build any recommender system specific code in crab? -sujit On Oct 8, 2013, at 2:51 PM, Joel Nothman <[email protected]> wrote: > On Tue, Oct 8, 2013 at 11:32 PM, Olivier Grisel <[email protected]> > wrote: > 2013/10/8 Gael Varoquaux <[email protected]>: > > On Tue, Oct 08, 2013 at 07:47:40AM +0200, Gilles Louppe wrote: > >> Unfortunately, algorithms for recommender systems are not planned in > >> scikit-learn in the short or mid-term. > > > > Indeed in the short term, but are we sure that we want to close the door > > to contributions implementing standard approaches for recommender > > systems? > > +1 for encouraging pull requests that implement recsys building blocks > (e.g. matrix factorization) that fit the scikit-learn API (fit and > partial_fit + predict or transform) and work with standard input > datastructures (e.g. input data is a scipy.sparse matrix or numpy > array). > > We don't want frameworkish code that hard code recsys specific > concepts (e.g. users and items) in the API though. > > I'm not familiar enough with recommender systems to understand whether any of > the existing matrix factorisations apply. Is this more a matter of presenting > an example of their application to this task? > > - Joel > ------------------------------------------------------------------------------ > October Webinars: Code for Performance > Free Intel webinars can help you accelerate application performance. > Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most from > the latest Intel processors and coprocessors. See abstracts and register > > http://pubads.g.doubleclick.net/gampad/clk?id=60134071&iu=/4140/ostg.clktrk_______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ October Webinars: Code for Performance Free Intel webinars can help you accelerate application performance. Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most from the latest Intel processors and coprocessors. See abstracts and register > http://pubads.g.doubleclick.net/gampad/clk?id=60134071&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
