On Wed, Aug 7, 2013 at 7:29 AM, <[email protected]> wrote: > 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. >> > > > So usually I do need to train a custom model for each user independently?
Not necessarily. Usually you need a global model that has user x item interaction variables. It isn't unusual to need a per user adjustment model, but if you can make that rare, you can do better. >From the linear user x item interaction model, for instance, you may be able to convert the model into a sparse weighted query that could retrieve items from an inverted index such as Solr. This might also be possible with a per user model, but I would have to think about that.
