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
