Created
February 10, 2019 17:03
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package br.com.kamila; | |
import org.apache.mahout.cf.taste.common.TasteException; | |
import org.apache.mahout.cf.taste.eval.RecommenderBuilder; | |
import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood; | |
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender; | |
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity; | |
import org.apache.mahout.cf.taste.model.DataModel; | |
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood; | |
import org.apache.mahout.cf.taste.recommender.Recommender; | |
import org.apache.mahout.cf.taste.recommender.UserBasedRecommender; | |
import org.apache.mahout.cf.taste.similarity.UserSimilarity; | |
//aqui connstruimos nosso recomendador | |
public class RecomendadorBuilder implements RecommenderBuilder { | |
public Recommender buildRecommender(DataModel model) throws TasteException { | |
//ele irá se basear na similaridade entre os usuários para contruir recomendações. | |
UserSimilarity similarity = new PearsonCorrelationSimilarity(model); | |
//pega a maior similaridade (vizinhança) entre os usuários do nosso modelo | |
UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model); | |
//fazemos nossa recomendação baseada no usuário, que é retornada no recommender | |
UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity); | |
return recommender; | |
} | |
} |
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