Hello Pat and thanks for your reply, I know that when users >> items normally item-based works better and I don't assume my similarity metric works better but I have, for research purposes, to compare:
- RMSE produced by a pearson correlation user-based algorithm VS - RMSE produced by a user-based algorithm where similarities are computed in a completely different and not standard way (algorithm implemented in C) so I am looking for a way to assign manually the user similarities; the test will be performed just on a couple of datasets so it's fine if I have to hard-code the assignment. Eugenio 2015-02-10 23:58 GMT+01:00 Pat Ferrel <[email protected]>: > There are many algorithms in Mahout but not all are equal. Some > combinations never perform well even though they are described in Mahout in > Action. The combination below is probably not the best. > > You seem to assume your user similarity metric is better than Mahout’s? Do > you have more users or items? > > If I were you I'd try user or item based recs in Mahout using LLR > similarity. It’s always performed best when I’ve compared. I say this > because I know of no way to do what you ask without writing some code and > partly because I bet it will outperform. > > Also be aware that the only good way to compare completely different > recommenders is A/B user testing. > > On Feb 10, 2015, at 3:39 AM, Eugenio Tacchini <[email protected]> > wrote: > > Hi all, > I am new to mahout but I work with recommender systems, I have just tried > to implement a simple user-based recommender: > > DataModel dm = new FileDataModel(new File("data/ratings.dat")); > > UserSimilarity similarity = new PearsonCorrelationSimilarity(dm); > > UserNeighborhood neighborhood = new > ThresholdUserNeighborhood(0.1,similarity, dm); > > UserBasedRecommender r = new GenericUserBasedRecommender(dm, neighborhood, > similarity); > > I would like to compare the results of this recommender with another I > implemented using another technology. The only differences between the two > algorithms is the way I choose neighbors; since I am not very fluent in > Java, instead of implementing the second algorithm in mahout, I would like > to manually specify the neighbors for each user, is this possible? Which is > the easiest way to provide an alternative user-user similarity matrix > (computed using my algorithm)? > > Just to recap: I want to use GenericUserBasedRecommender but providing an > alternative users similarity matrix, without reimplementing my similarity > algorithm in Java. Basically if I could import the similarities from a text > file it would be great, but other methods are fine as well. > > Thanks a lot in advance. > > Eugenio Tacchini > >
