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

Introduction

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

Usage

From source file GavaFactorizer.java

/** Matrix factorization with user and item biases for rating prediction, trained with plain vanilla SGD  */
public class GavaFactorizer extends AbstractFactorizer {

    /** Multiplicative decay factor for learning_rate */
    protected final double learningRateDecay;
    /** Learning rate (step size) */

From source file com.innometrics.integration.app.recommender.ml.als.ALSWRFactorizer.java

/**
 * factorizes the rating matrix using "Alternating-Least-Squares with Weighted--Regularization" as described in
 * <a href="http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf">
 * "Large-scale Collaborative Filtering for the Netflix Prize"</a>
 *
 *  also supports the implicit feedback variant of this approach as described in "Collaborative Filtering for Implicit

From source file com.predictionmarketing.itemrecommend.CliMF.java

/** Matrix factorization with user and item biases for rating prediction, trained with plain vanilla SGD  */
public class CliMF extends AbstractFactorizer {
    private class Usersitemidset {
        private long[] replyidset;
        private long[] noreplyidset;

From source file com.predictionmarketing.itemrecommend.RatingSGDFactorizer.java

/** Matrix factorization with user and item biases for rating prediction, trained with plain vanilla SGD  */
public class RatingSGDFactorizer extends AbstractFactorizer {

    protected static final int FEATURE_OFFSET = 2;

    /** Multiplicative decay factor for learning_rate */

From source file com.predictionmarketing.itemrecommend.SUSSGDFactorizer.java

/** Matrix factorization with user and item biases for rating prediction, trained with plain vanilla SGD  */
public class SUSSGDFactorizer extends AbstractFactorizer {
    private class Usersitemidset {
        private long[] replyidset;
        private long[] noreplyidset;

From source file org.mymedialite.examples.TasteFactorizer.java

/**
 * Adaptor class between MyMediaLite and Mahout.
 * Allows a subclass of org.mymedialite.itemrec.MF to be used to create a
 * org.apache.mahout.cf.taste.impl.recommender.svd.Factorization
 * @version 2.03
 */

From source file org.plista.kornakapi.core.optimizer.ErrorALSWRFactorizer.java

/**
 * factorizes the rating matrix using "Alternating-Least-Squares with Weighted--Regularization" as described in
 * <a href="http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf">
 * "Large-scale Collaborative Filtering for the Netflix Prize"</a>
 *
 *  also supports the implicit feedback variant of this approach as described in "Collaborative Filtering for Implicit