Example usage for org.apache.mahout.cf.taste.common Weighting UNWEIGHTED

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In this page you can find the example usage for org.apache.mahout.cf.taste.common Weighting UNWEIGHTED.

Prototype

Weighting UNWEIGHTED

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Usage

From source file:net.ufida.info.mahout.common.SlopeOneRecommender.java

License:Apache License

/**
 * <p>//from   w  ww .j  a va 2 s  .c om
 * Creates a  based on the given {@link DataModel}.
 * </p>
 *
 * <p>
 * If {@code weighted} is set, acts as a weighted slope one recommender. This implementation also
 * includes an experimental "standard deviation" weighting which weights item-item ratings diffs with lower
 * standard deviation more highly, on the theory that they are more reliable.
 * </p>
 *
 * @param weighting
 *          if {@link Weighting#WEIGHTED}, acts as a weighted slope one recommender
 * @param stdDevWeighting
 *          use optional standard deviation weighting of diffs
 * @throws IllegalArgumentException
 *           if {@code diffStorage} is null, or stdDevWeighted is set when weighted is not set
 */
public SlopeOneRecommender(DataModel dataModel, Weighting weighting, Weighting stdDevWeighting,
        DiffStorage diffStorage) {
    super(dataModel);
    Preconditions.checkArgument(stdDevWeighting != Weighting.WEIGHTED || weighting != Weighting.UNWEIGHTED,
            "weighted required when stdDevWeighted is set");
    Preconditions.checkArgument(diffStorage != null, "diffStorage is null");
    this.weighted = weighting == Weighting.WEIGHTED;
    this.stdDevWeighted = stdDevWeighting == Weighting.WEIGHTED;
    this.diffStorage = diffStorage;
}

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From source file:norbert.mynemo.core.recommendation.recommender.ItemSimilarityRecommender.java

License:Apache License

@Override
public Recommender buildRecommender(DataModel dataModel) throws TasteException {
    checkArgument(dataModel != null, "Data model type must not be null.");

    ItemSimilarity similarity = null;/*from   w  w w  .  j av  a2 s  . c o m*/
    switch (selectedSimilarity) {
    case ITEM_SIMILARITY_WITH_CITY_BLOCK_DISTANCE:
        similarity = new CityBlockSimilarity(dataModel);
        break;
    case ITEM_SIMILARITY_WITH_EUCLIDEAN_DISTANCE:
        similarity = new EuclideanDistanceSimilarity(dataModel, Weighting.UNWEIGHTED);
        break;
    case ITEM_SIMILARITY_WITH_LOG_LIKELIHOOD:
        similarity = new LogLikelihoodSimilarity(dataModel);
        break;
    case ITEM_SIMILARITY_WITH_PEARSON_CORRELATION:
        similarity = new PearsonCorrelationSimilarity(dataModel, Weighting.UNWEIGHTED);
        break;
    case ITEM_SIMILARITY_WITH_TANIMOTO_COEFFICIENT:
        similarity = new TanimotoCoefficientSimilarity(dataModel);
        break;
    case ITEM_SIMILARITY_WITH_UNCENTERED_COSINE:
        similarity = new UncenteredCosineSimilarity(dataModel, Weighting.UNWEIGHTED);
        break;
    case ITEM_SIMILARITY_WITH_WEIGHTED_EUCLIDEAN_DISTANCE:
        similarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
        break;
    case ITEM_SIMILARITY_WITH_WEIGHTED_PEARSON_CORRELATION:
        similarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
        break;
    case ITEM_SIMILARITY_WITH_WEIGHTED_UNCENTERED_COSINE:
        similarity = new UncenteredCosineSimilarity(dataModel, Weighting.WEIGHTED);
        break;
    default:
        throw new IllegalStateException();
    }

    int cacheSize = Math.min(dataModel.getNumItems() * dataModel.getNumItems(), MAXIMUM_CACHE_SIZE);
    similarity = new CachingItemSimilarity(similarity, cacheSize);

    return new GenericItemBasedRecommender(dataModel, similarity);
}

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From source file:norbert.mynemo.core.recommendation.recommender.UserSimilarityRecommender.java

License:Apache License

private UserSimilarity createSimilarity(DataModel dataModel) throws TasteException {
    UserSimilarity selectedSimilarity;//from   w w w  .  j a  va2 s . c  o  m

    switch (configuration.getType()) {
    case USER_SIMILARITY_WITH_CITY_BLOCK_DISTANCE:
        selectedSimilarity = new CityBlockSimilarity(dataModel);
        break;

    case USER_SIMILARITY_WITH_EUCLIDEAN_DISTANCE:
        selectedSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.UNWEIGHTED);
        break;

    case USER_SIMILARITY_WITH_LOG_LIKELIHOOD:
        selectedSimilarity = new LogLikelihoodSimilarity(dataModel);
        break;

    case USER_SIMILARITY_WITH_ORIGINAL_SPEARMAN_CORRELATION:
        selectedSimilarity = new OriginalSpearmanCorrelationSimilarity(dataModel);
        break;

    case USER_SIMILARITY_WITH_PEARSON_CORRELATION:
        selectedSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.UNWEIGHTED);
        break;

    case USER_SIMILARITY_WITH_SPEARMAN_CORRELATION:
        selectedSimilarity = new SpearmanCorrelationSimilarity(dataModel);
        break;

    case USER_SIMILARITY_WITH_TANIMOTO_COEFFICIENT:
        selectedSimilarity = new TanimotoCoefficientSimilarity(dataModel);
        break;

    case USER_SIMILARITY_WITH_UNCENTERED_COSINE:
        selectedSimilarity = new UncenteredCosineSimilarity(dataModel, Weighting.UNWEIGHTED);
        break;

    case USER_SIMILARITY_WITH_WEIGHTED_EUCLIDEAN_DISTANCE:
        selectedSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED);
        break;

    case USER_SIMILARITY_WITH_WEIGHTED_PEARSON_CORRELATION:
        selectedSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED);
        break;

    case USER_SIMILARITY_WITH_WEIGHTED_UNCENTERED_COSINE:
        selectedSimilarity = new UncenteredCosineSimilarity(dataModel, Weighting.WEIGHTED);
        break;

    default:
        throw new IllegalStateException();
    }

    int cacheSize = Math.min(dataModel.getNumUsers() * dataModel.getNumUsers(), MAXIMUM_CACHE_SIZE);

    return new CachingUserSimilarity(selectedSimilarity, cacheSize);
}

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From source file:recsys.similarity.TrueEuclideanDistanceSimilarity.java

License:Apache License

/**
 * @throws IllegalArgumentException//from  w  w w.  java  2  s  .  c  o  m
 *             if {@link DataModel} does not have preference values
 */
public TrueEuclideanDistanceSimilarity(DataModel dataModel) throws TasteException {
    this(dataModel, Weighting.UNWEIGHTED);
}

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