Example usage for org.apache.mahout.cf.taste.impl.recommender AbstractRecommender subclass-usage

Introduction

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

Usage

From source file com.recsys.svd.CustomSVDRecommender.java

/**
 * A {@link org.apache.mahout.cf.taste.recommender.Recommender} that uses matrix
 * factorization (a projection of users and items onto a feature space)
 */
public final class CustomSVDRecommender extends AbstractRecommender {
    public static Logger slf4jLogger = LoggerFactory.getLogger(CustomSVDRecommender.class);

From source file com.webir.popcornsaver.cluster.TreeClusteringRecommender.java

/**
 * <p>
 * A {@link org.apache.mahout.cf.taste.recommender.Recommender} that clusters users, then determines the
 * clusters' top recommendations. This implementation builds clusters by repeatedly merging clusters until
 * only a certain number remain, meaning that each cluster is sort of a tree of other clusters.
 * </p>

From source file de.unima.dws.webmining.rs.recommender.AvgUserPrefAdaptedUserBasedRecommender.java

/**
 * <p>
 * An extension to the simple {@link GenericUserBasedRecommender} which uses a
 * given {@link DataModel} and {@link UserNeighborhood} to produce
 * recommendations. In comparison to the {@link GenericUserBasedRecommender}
 * the average user preference is taken into account when calculating prediction.

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

/**
 * <p>
 * A basic "slope one" recommender. (See an <a href="http://www.daniel-lemire.com/fr/abstracts/SDM2005.html">
 * excellent summary here</a> for example.) This {@link org.apache.mahout.cf.taste.recommender.Recommender} is
 * especially suitable when user preferences are updating frequently as it can incorporate this information
 * without expensive recomputation.

From source file org.plista.kornakapi.core.recommender.FoldingFactorizationBasedRecommender.java

/** a matrix factorization based recommender that supports folding in new users */
public final class FoldingFactorizationBasedRecommender extends AbstractRecommender
        implements KornakapiRecommender {

    private FoldingFactorization foldingFactorization;
    private final PersistenceStrategy persistenceStrategy;

From source file recommender.CustomRecommender.java

/**
 * @author Daniele Cenni, daniele.cenni@unifi.it
 * {@link org.apache.mahout.cf.taste.recommender.Recommender} that uses matrix
 * factorization (a projection of users and items onto a feature space)
 */
public final class CustomRecommender extends AbstractRecommender {

From source file recommender.MyRecommender.java

/**
 * @author Daniele Cenni, daniele.cenni@unifi.it
 * {@link org.apache.mahout.cf.taste.recommender.Recommender} that uses matrix
 * factorization (a projection of users and items onto a feature space)
 */
public final class MyRecommender extends AbstractRecommender {

From source file smartcityrecommender.CustomRecommender.java

/**
 * @author Daniele Cenni, daniele.cenni@unifi.it
 * {@link org.apache.mahout.cf.taste.recommender.Recommender} that uses matrix
 * factorization (a projection of users and items onto a feature space)
 */
public final class CustomRecommender extends AbstractRecommender {

From source file smartcityrecommender.MyRecommender.java

/**
 * @author Daniele Cenni, daniele.cenni@unifi.it
 * {@link org.apache.mahout.cf.taste.recommender.Recommender} that uses matrix
 * factorization (a projection of users and items onto a feature space)
 */
public final class MyRecommender extends AbstractRecommender {