Yes, you want the sampling one so you can reduce the number of neighbors you consider.
On Fri, May 11, 2012 at 6:47 PM, Emilio Suarez <[email protected]> wrote: > Thanks Sean, > > So, do you suggest something like this? > > LogLikelihoodSimilarity similarity = new > LogLikelihoodSimilarity(fileDataModel); > PreferredItemsNeighborhoodCandidateItemsStrategy candidateStrategy = > new PreferredItemsNeighborhoodCandidateItemsStrategy(); > recommender = new GenericItemBasedRecommender(fileDataModel, > similarity, candidateStrategy, candidateStrategy); > > or this? > > LogLikelihoodSimilarity similarity = new > LogLikelihoodSimilarity(fileDataModel); > SamplingCandidateItemsStrategy candidateStrategy = new > SamplingCandidateItemsStrategy(); > recommender = new GenericItemBasedRecommender(fileDataModel, > similarity, candidateStrategy, candidateStrategy); > > > -emilio > > You need to apply a CandidateItemStrategy to reduce the number of > elements you consider, or else it will take a very long time because > almost the entire model is a candidate for recommendation. >
