Great!, trying that now… thanks again Sean! -emilio On May 11, 2012, at 11:50 AM, Sean Owen wrote:
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]<mailto:[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.
