2013/10/9 Eustache DIEMERT <[email protected]>: > Another issue that comes from times to times is the fitness of the sklearn > API wrt to recommendation tasks. > > I believe it's pretty good if one has to manipulate - e.g.factorize - (item, > user) matrices, but it falls short when dealing with explore/exploit > scenarios. > > An example of that is the bandit [1] family of algorithms, where one knows > the payoff of an action iff the action is chosen by the algorithm as the > next step. > > [1] http://en.wikipedia.org/wiki/Multi-armed_bandit
I agree, and evolving the API to address the multi armed bandit / reinforcement learning kind of task is probably out of the scope of the scikit-learn project (at least for in the short and medium terms). Something that would help for the recsys / personalization kind of applications though would be to make it easier to address learning-to-rank problems for instance using pairwise reductions as blogged by Fabian some time ago: http://fa.bianp.net/blog/2012/learning-to-rank-with-scikit-learn-the-pairwise-transform/ Note that pointwise regression models such as GradientBoostedRegressor, ExtraTreesRegressor or even penalized linear regression models with suitable features would already work as good baselines to predict click through rates or relevance scores to rank query (recommendations) provided that you can collect to this kind of supervised / feedback signal from a production recsys. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ 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
