Examples of TanimotoCoefficientSimilarity

org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity
kipedia.org/wiki/Jaccard_index#Tanimoto_coefficient_.28extended_Jaccard_coefficient.29"> Tanimoto coefficient, or extended Jaccard coefficient.

This is intended for "binary" data sets where a user either expresses a generic "yes" preference for an item or has no preference. The actual preference values do not matter here, only their presence or absence.

The value returned is in [0,1].

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

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

    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
        new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));


    DataModel dataModel = new GenericDataModel(userData);
    ItemBasedRecommender recommender =
        new GenericItemBasedRecommender(dataModel, new TanimotoCoefficientSimilarity(dataModel));


    BatchItemSimilarities batchSimilarities = new MultithreadedBatchItemSimilarities(recommender, 10);


    batchSimilarities.computeItemSimilarities(1, 1, mock(SimilarItemsWriter.class));
  }

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

    userData.put(2, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2, 1, 1),
        new GenericPreference(2, 2, 1), new GenericPreference(2, 4, 1))));


    DataModel dataModel = new GenericDataModel(userData);
    ItemBasedRecommender recommender =
        new GenericItemBasedRecommender(dataModel, new TanimotoCoefficientSimilarity(dataModel));


    BatchItemSimilarities batchSimilarities = new MultithreadedBatchItemSimilarities(recommender, 10);


    try {
      // Batch size is 100, so we only get 1 batch from 3 items, but we use a degreeOfParallelism of 2

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

    switch (params.itemSimilarity) {
      case MahoutAlgoParams.LOG_LIKELIHOOD:
        similarity = new LogLikelihoodSimilarity(dataModel);
        break;
      case MahoutAlgoParams.TANIMOTO_COEFFICIENT:
        similarity = new TanimotoCoefficientSimilarity(dataModel);
        break;
      default:
        logger.error("Invalid itemSimilarity: " + params.itemSimilarity +
          ". LogLikelihoodSimilarity is used.");
        similarity = new LogLikelihoodSimilarity(dataModel);

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