FunkSVD is a suboptimal duplicate of RatingSGDFactorizer, ImplicitLinearRegressionFactorizer is a duplicate of ALSWR so I think we should only keep one of each.
The other three recommenders seem to be used almost never, so I'd like to remove them, however I wouldn't have a problem with keeping them for any reason. Best, Sebastian On 06.12.2012 16:14, Sean Owen wrote: > The tree-based ones are very old and not fast, and were more of an > experiment. I recall a few questions about them but it seemed like > people were really just trying to do clustering, and this is a bad way > to do clustering. > > knn is old too, and in a sense spiritually quite similar to ALS. I > don't mind removing it either. > > It would seal it if there were even a nominal argument that this > improves the rest of the code base -- less to maintain, removes > duplication, inconsistency, etc. I could imagine that argument here. > > On Thu, Dec 6, 2012 at 3:06 PM, Sebastian Schelter <[email protected]> wrote: >> Hi there, >> >> I'm currently thinking whether we should do a little cleanup in the >> non-distributed recommenders package and throw out recommenders that >> have not been used/asked about on the mailinglist or that have been >> replaced by a superior implementation. >> >> If anyone reads this and sees a recommender, he/she wants to be kept, >> please shout! >> >> /s >> >> Here's a list of suggested stuff to remove, let me know what you think: >> >> org.apache.mahout.cf.taste.impl.recommender.svd.FunkSVDFactorizer >> >> RatingSGDFactorizer should be learning faster and has a nicer model as >> it includes user/item biases >> >> >> org.apache.mahout.cf.taste.impl.recommender.svd.ImplicitLinearRegressionFactorizer >> >> Seems to be using the same model as ALSWRFactorizer, however there are >> no tests and ALSWR can handle more explicit and implicit feedback >> >> >> org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender >> org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender2 >> org.apache.mahout.cf.taste.impl.recommender.knn >> >> I don't recall anybody using those or asking about them the last years. >> >> >>
