Examples of LogLikelihoodSimilarity

Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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import org.apache.mahout.cf.taste.similarity.ItemSimilarity;

class KnnBasedRecommender {

  Recommender buildRecommender(DataModel model) {
    ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
    Optimizer optimizer = new ConjugateGradientOptimizer();
    return new KnnItemBasedRecommender(model, similarity, optimizer, 10);
  }
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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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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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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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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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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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public final class BookCrossingBooleanRecommender implements Recommender {

  private final Recommender recommender;

  public BookCrossingBooleanRecommender(DataModel bcModel) throws TasteException {
    UserSimilarity similarity = new CachingUserSimilarity(new LogLikelihoodSimilarity(bcModel), bcModel);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(10, Double.NEGATIVE_INFINITY, similarity, bcModel, 1.0);
    recommender = new GenericBooleanPrefUserBasedRecommender(bcModel, neighborhood, similarity);
  }
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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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public final class BookCrossingBooleanRecommender implements Recommender {

  private final Recommender recommender;

  public BookCrossingBooleanRecommender(DataModel bcModel) throws TasteException {
    UserSimilarity similarity = new CachingUserSimilarity(new LogLikelihoodSimilarity(bcModel), bcModel);
    UserNeighborhood neighborhood =
        new NearestNUserNeighborhood(10, Double.NEGATIVE_INFINITY, similarity, bcModel, 1.0);
    recommender = new GenericBooleanPrefUserBasedRecommender(bcModel, neighborhood, similarity);
  }
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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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  private final ItemSimilarity cfSimilarity;
  private final ItemSimilarity contentSimilarity;

  HybridSimilarity(DataModel dataModel, File dataFileDirectory) throws IOException {
    super(dataModel);
    cfSimilarity = new LogLikelihoodSimilarity(dataModel);
    contentSimilarity = new TrackItemSimilarity(dataFileDirectory);
  }
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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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  public void testBoolean() throws Exception {
    DataModel model = getBooleanDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) {
        return new GenericBooleanPrefItemBasedRecommender(dataModel, new LogLikelihoodSimilarity(dataModel));
      }
    };
    DataModelBuilder dataModelBuilder = new DataModelBuilder() {
      @Override
      public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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      resultFile.delete();
    }

    DataModel dataModel = new GroupLensDataModel(new File(args[0]));
    ItemBasedRecommender recommender = new GenericItemBasedRecommender(dataModel,
        new LogLikelihoodSimilarity(dataModel));
    BatchItemSimilarities batch = new MultithreadedBatchItemSimilarities(recommender, 5);

    int numSimilarities = batch.computeItemSimilarities(Runtime.getRuntime().availableProcessors(), 1,
        new FileSimilarItemsWriter(resultFile));
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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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      resultFile.delete();
    }

    DataModel dataModel = new GroupLensDataModel(new File(args[0]));
    ItemBasedRecommender recommender = new GenericItemBasedRecommender(dataModel,
        new LogLikelihoodSimilarity(dataModel));
    BatchItemSimilarities batch = new MultithreadedBatchItemSimilarities(recommender, 5);

    int numSimilarities = batch.computeItemSimilarities(Runtime.getRuntime().availableProcessors(), 1,
        new FileSimilarItemsWriter(resultFile));
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Examples of org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity

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  public void testBoolean() throws Exception {
    DataModel model = getBooleanDataModel();
    RecommenderBuilder builder = new RecommenderBuilder() {
      @Override
      public Recommender buildRecommender(DataModel dataModel) {
        return new GenericBooleanPrefItemBasedRecommender(dataModel, new LogLikelihoodSimilarity(dataModel));
      }
    };
    DataModelBuilder dataModelBuilder = new DataModelBuilder() {
      @Override
      public DataModel buildDataModel(FastByIDMap<PreferenceArray> trainingData) {
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