Example usage for org.apache.mahout.cf.taste.impl.recommender ItemAverageRecommender ItemAverageRecommender

Introduction

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

Prototype

public ItemAverageRecommender(DataModel dataModel) throws TasteException 

Source Link

Usage

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   ww w.j ava  2  s. co  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:norbert.mynemo.core.recommendation.recommender.BasicRecommender.java

License:Apache License

@Override
public Recommender buildRecommender(DataModel dataModel) throws TasteException {

    switch (recommender) {
    case ITEM_AVERAGE:
        return new ItemAverageRecommender(dataModel);
    case RANDOM://from  w w w  .ja v a2s .  c  om
        return new RandomRecommender(dataModel);
    case USER_AVERAGE:
        return new ItemUserAverageRecommender(dataModel);
    default:
        throw new IllegalStateException();
    }
}

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