On Tue, Aug 6, 2013 at 5:27 PM, Dominik Hübner <[email protected]> wrote:
> I wonder how model based approaches might be scaled to a large number of > users. My understanding is that I would have to train some model like a > decision tree or naive bayes (or regression … etc.) for each user and do > the prediction for each item using this model. > > Is there any common approach to get those techniques scaling up with > larger datasets? > Yes. There are several approaches. One of the most effective is rescoring. You use a performant recommender such as a search engine based recommender and then rescore the top few hundred items using a more detailed model. This typically won't be fast enough if you have something like a random forest, but if your final targeting model is logistic regression, it probably will be fast enough. In any case, there are also tricks you can pull in the evaluation of certain classes of models. For instance, with logistic regression, you can remove the link function (doesn't change ordering) and you can ignore all user specific features and weights (this doesn't change ordering either). This leaves you with a relatively small number of computations in the form of a sparse by dense dot product.
