Example usage for org.apache.mahout.cf.taste.impl.similarity TanimotoCoefficientSimilarity TanimotoCoefficientSimilarity

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.impl.similarity TanimotoCoefficientSimilarity TanimotoCoefficientSimilarity.

Prototype

public TanimotoCoefficientSimilarity(DataModel dataModel) 

Source Link

Usage

From source file:be.ugent.tiwi.sleroux.newsrec.newsreccollaborativefiltering.MahoutTermRecommender.java

public Map<Long, List<RecommendedItem>> makeRecommendations(int n) throws IOException, TasteException {

    DataModel model = new FileDataModel(new File(mahoutInputFile), ";");
    UserSimilarity similarity = new TanimotoCoefficientSimilarity(model);
    UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
    Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);

    LongPrimitiveIterator it = model.getUserIDs();
    Map<Long, List<RecommendedItem>> output = new HashMap<>(model.getNumUsers());

    while (it.hasNext()) {
        long user = it.nextLong();
        List<RecommendedItem> items = recommender.recommend(user, n);
        output.put(user, items);/*ww w.  jav  a 2 s .com*/
    }

    return output;
}

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From source file:businessreco.BusinessReco.java

public static void main(String args[]) {

    try {//from  ww  w .ja  v a2s.c  om

        //Loading the DATA;    

        DataModel dm = new FileDataModel(new File(
                "C:\\Users\\bryce\\Course Work\\3. Full Summer\\Big Data\\Final Project\\Yelp\\FINAL CODE\\Mahout\\data\\busirec_new.csv"));

        // We use the below line to relate businesses. 
        //ItemSimilarity sim = new LogLikelihoodSimilarity(dm);

        TanimotoCoefficientSimilarity sim = new TanimotoCoefficientSimilarity((dm));

        //Using the below line get recommendations
        GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(dm, sim);

        //Looping through every business.
        for (LongPrimitiveIterator items = dm.getItemIDs(); items.hasNext();) {
            long itemId = items.nextLong();

            // For each business we recommend 3 businesses.

            List<RecommendedItem> recommendations = recommender.mostSimilarItems(itemId, 2);

            for (RecommendedItem recommendation : recommendations) {

                System.out.println(itemId + "," + recommendation.getItemID() + "," + recommendation.getValue());

            }

        }
    }

    catch (IOException | TasteException e) {
        System.out.println(e);
    }

}

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From source file:com.anjuke.romar.mahout.GenericReloadDataModelTest.java

License:Apache License

@Test
public void test() throws TasteException {
    GenericReloadDataModel model = new GenericReloadDataModel();
    ItemBasedRecommender recommender = new GenericItemBasedRecommender(model,
            new CachingItemSimilarity(new TanimotoCoefficientSimilarity(model), 100));
    recommender.setPreference(1, 1, 5f);
    recommender.setPreference(1, 2, 1f);
    recommender.setPreference(2, 1, 1f);
    recommender.setPreference(2, 2, 5f);
    recommender.refresh(null);//from   w  w w. j a  v  a 2 s . c o m
    assertEquals(0, recommender.recommend(1, 5).size());
    recommender.setPreference(2, 3, 1f);
    recommender.refresh(null);
    assertEquals(1, recommender.recommend(1, 5).size());
    recommender.setPreference(2, 4, 1f);
    recommender.refresh(null);
    assertEquals(2, recommender.recommend(1, 5).size());
    assertEquals(2, recommender.recommend(1, 5).size());
}

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From source file:com.corchado.testRecomender.recomendador.java

/**
 * @param args the command line arguments
 * @throws java.io.IOException/*w ww .ja  va2s  . c om*/
 * @throws org.apache.mahout.cf.taste.common.TasteException
 */
public static void main(String[] args) throws IOException, TasteException {
    final Scanner entrada = new Scanner(System.in);

    DataModel model = new FileDataModel(new File("ml-100k/ua.base"));
    UserSimilarity similarity = null;
    int obciones;
    System.out.println("RECOMENDADOR MEDIANTE FILTRADO COLABORATIVO");
    do {
        System.out.println("Seleccione la funcion de similitud a utilizar para recomendar");
        System.out.println("marque 1 para la funcion euclidiana");
        System.out.println("marque 2 para la funcion de Pearson");
        System.out.println("marque 3 para la funcion de Log-likelihood");
        System.out.println("marque 4 para la funcion de Tanimoto");

        obciones = entrada.nextInt();
        switch (obciones) {
        case 1:
            similarity = new EuclideanDistanceSimilarity(model);
            break;
        case 2:
            similarity = new PearsonCorrelationSimilarity(model);
            break;
        case 3:
            similarity = new LogLikelihoodSimilarity(model);
            break;
        case 4:
            similarity = new TanimotoCoefficientSimilarity(model);
            break;
        }
    } while (obciones != 1 && obciones != 2 && obciones != 3 && obciones != 4);

    int queHago = 0;
    try {

        do {
            System.out.println("\n Seleccione... ");
            System.out.println("1 para realizar una recomendacion ");
            System.out.println("2 si quiere probar una recomendacion ");
            System.out.println("3 para evaluar precision y recall");
            System.out.println("4 si quiere salir");
            queHago = entrada.nextInt();
            switch (queHago) {
            case 1:
                System.out.println("Recomendando...");
                Recomendar(entrada, model, similarity);
                break;
            case 2:
                System.out.println(
                        "Realizando una prueba al recomendador tomado como conjunto de entrenamiento el 70% "
                                + "del dataset y el 30% como conjunto de prueba. Ademas se evalua mediante el promedio de las diferencias."
                                + "Mientras mas pequeo es este valor mejor es la recomendacion");
                System.out.println("Si el dataset es muy grande la evaluacion puede tardar un poquito..");
                Probar(entrada, model, similarity);
                break;
            case 3:
                System.out.println(
                        "Evaluando precision y recall tomando como limite entre una recomendacion bueno o mala "
                                + "la suma entre la media y la desviaci'on estandar (threshold =  + ?)...");
                evaluarPrecicionRecall(entrada, model, similarity);
                break;
            }
        } while (queHago != 4);
    } catch (IOException | TasteException e) {
        System.out.println(e.getMessage());
    }
}

---

From source file:com.corchado.testRecomender.recomendadorUI.java

private String funcionSimilitud() throws TasteException {
    String funcionSimilitud = SpinnerFuncionsmilitud.getValue().toString();

    switch (funcionSimilitud) {
    case "Distancia Euclideana":
        similarity = new EuclideanDistanceSimilarity(model);
        break;/*from  www .j av  a  2s .  c  o  m*/
    case "Correlacin de Pearson":
        similarity = new PearsonCorrelationSimilarity(model);
        break;
    case "log-likelihood":
        similarity = new LogLikelihoodSimilarity(model);
        break;
    case "Coheficiente de Tanimoto":
        similarity = new TanimotoCoefficientSimilarity(model);
        break;
    }
    return funcionSimilitud;
}

---

From source file:com.github.gurelkaynak.recommendationengine.core.RecommenderFactory.java

public ItemBasedRecommender buildItemBasedRecommender(DataModel dataModel) {
    ItemBasedRecommender recommender;//w  w  w .j a  v a2  s.co m
    recommender = null;

    try {
        ItemSimilarity similarity = new CachingItemSimilarity(new TanimotoCoefficientSimilarity(dataModel),
                dataModel);
        recommender = new GenericItemBasedRecommender(dataModel, similarity);

    } catch (TasteException exception) {
        System.err.println(exception);
    }

    return recommender;
}

---

From source file:com.mycompany.xplor_recommendation_engine.Xplor.java

/**
 * @param args the command line arguments
 * @throws java.sql.SQLException//from  w  w w  .j ava  2s .co  m
 * @throws java.io.IOException
 */
public static void main(String[] args) throws SQLException, IOException, TasteException {

    FileConverter fc = FileConverter.getFileConverter();
    int[] columnSpecs = new int[2];
    columnSpecs[0] = 2;
    columnSpecs[1] = 2;
    fc.convertToCSV("xplor_development", "blog_profile_maps", columnSpecs);

    // Calibrate recommender for csv file
    DataModel model = new FileDataModel(new File("blog_profile_maps.csv"));
    UserSimilarity similarity = new TanimotoCoefficientSimilarity(model);
    UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
    Recommender recommender = new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity);
    Recommender cachingRecommender = new CachingRecommender(recommender);

    // Establish JDBC connection

    for (int i = 1; i <= model.getNumUsers(); i++) {
        List<RecommendedItem> recommendations = cachingRecommender.recommend(i, 10);
        for (RecommendedItem recommendation : recommendations) {
            // Store recommendations in recommendations table
        }
    }

    // Store recommendations in database
}

---

From source file:edu.nudt.c6.datasetlinking.mahout.MyRecommenderBuilder.java

License:Apache License

@Override
public Recommender buildRecommender(DataModel dataModel) throws TasteException {
    if (recommenderType == RECOMMENDER.ITEM) {
        ItemSimilarity itemSimilarity = null;
        switch (similarityType) {
        case PEARSON:
            itemSimilarity = new PearsonCorrelationSimilarity(dataModel);
            break;
        case PEARSON_WEIGHTED:
            itemSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case COSINE:
            itemSimilarity = new UncenteredCosineSimilarity(dataModel);
            break;
        case TANIMOTO:
            itemSimilarity = new TanimotoCoefficientSimilarity(dataModel);
            break;
        case LOGLIKELIHOOD:
            itemSimilarity = new LogLikelihoodSimilarity(dataModel);
            break;
        case CITYBLOCK:
            itemSimilarity = new CityBlockSimilarity(dataModel);
            break;
        case EUCLIDEAN:
            itemSimilarity = new EuclideanDistanceSimilarity(dataModel);
            break;
        case EUCLIDEAN_WEIGHTED:
            itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case DATASET_VOCABULARY_COSINE:
            try {
                itemSimilarity = new DatasetVocabularySimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();//from  www  .jav a2  s  . c  o  m
            }
            break;
        case DATASET_CLASS_COSINE:
            try {
                itemSimilarity = new DatasetClassSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_SUBJECTS:
            try {
                itemSimilarity = new DatasetPropertySubjectsSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_TRIPLES:
            try {
                itemSimilarity = new DatasetPropertyTriplesSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        default:
            itemSimilarity = new EuclideanDistanceSimilarity(dataModel);
        }

        if (pref) {
            return new GenericItemBasedRecommender(dataModel, itemSimilarity);
        } else {
            return new GenericBooleanPrefItemBasedRecommender(dataModel, itemSimilarity);
        }

    } else if (recommenderType == RECOMMENDER.USER) {
        UserSimilarity userSimilarity = null;
        switch (similarityType) {
        case PEARSON:
            userSimilarity = new PearsonCorrelationSimilarity(dataModel);
            break;
        case PEARSON_WEIGHTED:
            userSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case COSINE:
            userSimilarity = new UncenteredCosineSimilarity(dataModel);
            break;
        case SPEARMAN:
            userSimilarity = new SpearmanCorrelationSimilarity(dataModel);
            break;
        case TANIMOTO:
            userSimilarity = new TanimotoCoefficientSimilarity(dataModel);
            break;
        case LOGLIKELIHOOD:
            userSimilarity = new LogLikelihoodSimilarity(dataModel);
            break;
        case CITYBLOCK:
            userSimilarity = new CityBlockSimilarity(dataModel);
            break;
        case EUCLIDEAN:
            userSimilarity = new EuclideanDistanceSimilarity(dataModel);
            break;
        case EUCLIDEAN_WEIGHTED:
            userSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
            break;
        case DATASET_VOCABULARY_COSINE:
            try {
                userSimilarity = new DatasetVocabularySimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_CLASS_COSINE:
            try {
                userSimilarity = new DatasetClassSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_SUBJECTS:
            try {
                userSimilarity = new DatasetPropertySubjectsSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case DATASET_PPROPERTY_COSINE_TRIPLES:
            try {
                userSimilarity = new DatasetPropertyTriplesSimilarity(dataModel);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        default:
            userSimilarity = new EuclideanDistanceSimilarity(dataModel);
        }

        UserNeighborhood userNeighborhood = null;
        switch (neighborhoodType) {
        case NEAREST:
            userNeighborhood = new NearestNUserNeighborhood(this.nearestNum, userSimilarity, dataModel);
            break;
        case THRESHOLD:
        default:
            userNeighborhood = new ThresholdUserNeighborhood(this.neighborThreshold, userSimilarity, dataModel);
        }

        if (pref) {
            return new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
        } else {
            return new GenericBooleanPrefUserBasedRecommender(dataModel, userNeighborhood, userSimilarity);
        }
    } else if (recommenderType == RECOMMENDER.SVD) {
        AbstractFactorizer factorizer = null;

        switch (SVDfactorizerType) {
        case RatingSGD:
            factorizer = new RatingSGDFactorizer(dataModel, factorNum, iterationNum);
            break;
        case ALSWR:
            factorizer = new ALSWRFactorizer(dataModel, factorNum, lambda, iterationNum);
            break;
        case SVDPlusPlus:
            factorizer = new SVDPlusPlusFactorizer(dataModel, factorNum, iterationNum);
            break;
        case ParallelSGD:
            factorizer = new ParallelSGDFactorizer(dataModel, factorNum, lambda, iterationNum);
            break;
        case MyRatingSGD:
            factorizer = new MyRatingSGDFactorizer(dataModel, factorNum, iterationNum);
            break;
        }

        return new SVDRecommender(dataModel, factorizer);

    } else if (recommenderType == RECOMMENDER.LINKDOCUMENT) {

    } else if (recommenderType == RECOMMENDER.CollaborativeRanking) {
        AbstractCRFactorizer factorizer = null;

        switch (CRFactorizerType) {
        case BasicLFM:
            try {
                factorizer = new BasicLFMFactorizer(dataModel, factorNum, iterationNum);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        case LFMTrans:
            try {
                factorizer = new LFMTransFactorizer(dataModel, factorNum, iterationNum, learningRate);
            } catch (ClassNotFoundException | IOException e) {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            break;
        }
        return new CollaborativeRankingRecommender(dataModel, factorizer);
    } else if (recommenderType == RECOMMENDER.Random) {
        return new RandomRecommender(dataModel);
    } else if (recommenderType == RECOMMENDER.ItemAverage) {
        return new ItemAverageRecommender(dataModel);
    } else if (recommenderType == RECOMMENDER.ItemUserAverage) {
        return new ItemUserAverageRecommender(dataModel);
    }

    return null;

}

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From source file:edu.uci.ics.sourcerer.ml.ApiRecommender.java

License:Open Source License

public ApiRecommender(DataModel bcModel) throws TasteException {

    //      UserSimilarity similarity = new EuclideanDistanceSimilarity(bcModel,Weighting.WEIGHTED);
    //      UserSimilarity similarity = new LogLikelihoodSimilarity(bcModel);
    //UserSimilarity similarity = new PearsonCorrelationSimilarity(bcModel);

    UserSimilarity similarity = new TanimotoCoefficientSimilarity(bcModel);

    //      UserSimilarity similarity = new MySqlTanimotoCoefficientUserSimilarity(
    //            ManualPoolingMysqlDataSourceFactory.getDataSource(
    //                  "jdbc:mysql://mondego.calit2.uci.edu:3307/sourcerer_test", 
    //                  "sourcerer", 
    //                  "")); 

    //      UserSimilarity similarity = new HammingDistanceSimilarity(bcModel);

    UserNeighborhood neighborhood = new NearestNUserNeighborhood(15, 0.4, similarity, bcModel, 1.0);
    recommender = new CachingRecommender(new GenericUserBasedRecommender(bcModel, neighborhood, similarity));
}

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From source file:edu.uci.ics.sourcerer.ml.db.tools.SimilarUserCalculatorTest.java

License:Open Source License

@Before
public void setUp() throws FileNotFoundException, TasteException {
    startTime = System.currentTimeMillis();
    File dataFile = new File(dataFileLocation);
    DataModel dm = new TasteFileModelWithStringItemIds(dataFile);
    suCalc.setDataModel(dm);/*from   ww  w  . j a v  a2s .  c o m*/
    suCalc.setNeighborhoodSize(100);
    suCalc.setSimilarityThreshold(0.3);
    suCalc.setUserSimilarity(new TanimotoCoefficientSimilarity(dm));

    suCalc2.setDataModel(dm);
    suCalc2.setNeighborhoodSize(100);
    suCalc2.setSimilarityThreshold(0.3);
    suCalc2.setUserSimilarity(new HammingDistanceSimilarity(dm));

    // suCalc.setUserSimilarity(new HammingDistanceSimilarity(dm));
    // suCalc.setUserSimilarity(new PearsonCorrelationSimilarity(dm));
    // suCalc.setUserSimilarity(new LogLikelihoodSimilarity(dm));
    System.out.println("Setup time (hh:mm:ss) = " + TimeUtil.formatMs(System.currentTimeMillis() - startTime));
    startTime = System.currentTimeMillis();

}

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