Package org.apache.mahout.cf.taste.similarity

Examples of org.apache.mahout.cf.taste.similarity.UserSimilarity

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    this(new FileDataModel(readResourceToTempFile("ratings.dat")));
  }

  public LibimsetiRecommender(DataModel model)
      throws TasteException, IOException {
    UserSimilarity similarity = new EuclideanDistanceSimilarity(model);
    UserNeighborhood neighborhood =
        new NearestNUserNeighborhood(2, similarity, model);
    delegate =
        new GenericUserBasedRecommender(model, neighborhood, similarity);
    this.model = model;
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      RecommenderIRStatsEvaluator evaluator =
        new GenericRecommenderIRStatsEvaluator();
      RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
        @Override
        public Recommender buildRecommender(DataModel model) throws TasteException {
          UserSimilarity similarity = new TanimotoCoefficientSimilarity(model);
          UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
          return new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
        }
      };
      IRStatistics stats = evaluator.evaluate(recommenderBuilder, null, model, null, 10, Double.NaN, 0.1);
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    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(100, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
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import org.apache.mahout.cf.taste.similarity.UserSimilarity;

class ClusterBasedRecommender {

  Recommender buildRecommender(DataModel model) throws TasteException {
    UserSimilarity similarity = new LogLikelihoodSimilarity(model);
    ClusterSimilarity clusterSimilarity =
        new FarthestNeighborClusterSimilarity(similarity);
    return new TreeClusteringRecommender(model, clusterSimilarity, 10);
  }
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  private GroupLensDataModelIntro() {
  }

  public static void main(String[] args) throws Exception {
    DataModel model = new GroupLensDataModel(new File("ratings.dat"));
    UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
    UserNeighborhood neighborhood =
      new NearestNUserNeighborhood(100, similarity, model);
    Recommender recommender =
      new GenericUserBasedRecommender(model, neighborhood, similarity);
    LoadEvaluator.runLoad(recommender);
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      new AverageAbsoluteDifferenceRecommenderEvaluator();
    // Build the same recommender for testing that we did last time:
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(2, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
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    RecommenderEvaluator evaluator =
      new AverageAbsoluteDifferenceRecommenderEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(10, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
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      new GenericRecommenderIRStatsEvaluator();
    // Build the same recommender for testing that we did last time:
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(2, similarity, model);
        return new GenericUserBasedRecommender(model, neighborhood, similarity);
      }
    };
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    RecommenderIRStatsEvaluator evaluator =
      new GenericRecommenderIRStatsEvaluator();
    RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel model) throws TasteException {
        UserSimilarity similarity = new LogLikelihoodSimilarity(model);
        UserNeighborhood neighborhood =
          new NearestNUserNeighborhood(10, similarity, model);
        return new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
      }
    };
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  @Override
  public long[] getUserNeighborhood(long userID) throws TasteException {
   
    DataModel dataModel = getDataModel();
    UserSimilarity userSimilarityImpl = getUserSimilarity();
   
    TopItems.Estimator<Long> estimator = new Estimator(userSimilarityImpl, userID, minSimilarity);
   
    LongPrimitiveIterator userIDs = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel.getUserIDs(),
      getSamplingRate());
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Related Classes of org.apache.mahout.cf.taste.similarity.UserSimilarity

  • com.manning.hip.ch9.MovieUserEvaluator$MyRecommendBuilder
  • com.manning.hip.ch9.MovieUserRecommender
  • mia.recommender.ch02.EvaluatorIntro
  • mia.recommender.ch02.IREvaluatorIntro
  • mia.recommender.ch02.UserBaseRecommenderEvaluation
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro1
  • mia.recommender.ch03.IREvaluatorBooleanPrefIntro2
  • mia.recommender.ch04.ClusterBasedRecommender
  • mia.recommender.ch04.GroupLens10MEvalIntro
  • mia.recommender.ch04.GroupLensDataModelIntro
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