org.lenskit.mf.funksvd

Class FunkSVDModelProvider

java.lang.Object

org.lenskit.mf.funksvd.FunkSVDModelProvider

All Implemented Interfaces:

javax.inject.Provider<FunkSVDModel>

public class FunkSVDModelProvider
extends Object
implements javax.inject.Provider<FunkSVDModel>

SVD recommender builder using gradient descent (Funk SVD).

This recommender builder constructs an SVD-based recommender using gradient descent, as pioneered by Simon Funk. It also incorporates the regularizations Funk did. These are documented in Netflix Update: Try This at Home. This implementation is based in part on Timely Development's sample code.

Field Summary

Fields

Modifier and Type	Field and Description
protected int	featureCount
protected double	initialValue
protected FunkSVDUpdateRule	rule
protected RatingMatrix	snapshot

Constructor Summary

Constructors

Constructor and Description
FunkSVDModelProvider(RatingMatrix snapshot, FunkSVDUpdateRule rule, int featureCount, double initVal)

Method Summary

Modifier and Type	Method and Description
protected double	doFeatureIteration(TrainingEstimator estimates, List<RatingMatrixEntry> ratings, org.apache.commons.math3.linear.RealVector userFeatureVector, org.apache.commons.math3.linear.RealVector itemFeatureVector, double trail) Do a single feature iteration.
FunkSVDModel	get()
protected void	summarizeFeature(org.apache.commons.math3.linear.RealVector ufv, org.apache.commons.math3.linear.RealVector ifv, FeatureInfo.Builder fib) Add a feature’s summary to the feature info builder.
protected void	trainFeature(int feature, TrainingEstimator estimates, org.apache.commons.math3.linear.RealVector userFeatureVector, org.apache.commons.math3.linear.RealVector itemFeatureVector, FeatureInfo.Builder fib) Train a feature using a collection of ratings.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

featureCount

protected final int featureCount

snapshot

protected final RatingMatrix snapshot

initialValue

protected final double initialValue

rule

protected final FunkSVDUpdateRule rule

Constructor Detail

FunkSVDModelProvider

@Inject
public FunkSVDModelProvider(@Nonnull
                                     RatingMatrix snapshot,
                                     @Nonnull
                                     FunkSVDUpdateRule rule,
                                     @FeatureCount
                                     int featureCount,
                                     @InitialFeatureValue
                                     double initVal)

Method Detail

get

public FunkSVDModel get()

Specified by:

get in interface javax.inject.Provider<FunkSVDModel>

trainFeature

protected void trainFeature(int feature,
                            TrainingEstimator estimates,
                            org.apache.commons.math3.linear.RealVector userFeatureVector,
                            org.apache.commons.math3.linear.RealVector itemFeatureVector,
                            FeatureInfo.Builder fib)

Train a feature using a collection of ratings. This method iteratively calls doFeatureIteration(TrainingEstimator, List, RealVector, RealVector, double) to train the feature. It can be overridden to customize the feature training strategy.

We use the estimator to maintain the estimate up through a particular feature value, rather than recomputing the entire kernel value every time. This hopefully speeds up training. It means that we always tell the updater we are training feature 0, but use a subvector that starts with the current feature.

Parameters:

feature - The number of the current feature.

estimates - The current estimator. This method is not expected to update the estimator.

userFeatureVector - The user feature values. This has been initialized to the initial value, and may be reused between features.

itemFeatureVector - The item feature values. This has been initialized to the initial value, and may be reused between features.

fib - The feature info builder. This method is only expected to add information about its training rounds to the builder; the caller takes care of feature number and summary data.

See Also:

doFeatureIteration(TrainingEstimator, List, RealVector, RealVector, double), summarizeFeature(RealVector, RealVector, FeatureInfo.Builder)

doFeatureIteration

protected double doFeatureIteration(TrainingEstimator estimates,
                                    List<RatingMatrixEntry> ratings,
                                    org.apache.commons.math3.linear.RealVector userFeatureVector,
                                    org.apache.commons.math3.linear.RealVector itemFeatureVector,
                                    double trail)

Do a single feature iteration.

Parameters:

estimates - The estimates.

ratings - The ratings to train on.

userFeatureVector - The user column vector for the current feature.

itemFeatureVector - The item column vector for the current feature.

trail - The sum of the remaining user-item-feature values.

Returns:

The RMSE of the feature iteration.

summarizeFeature

protected void summarizeFeature(org.apache.commons.math3.linear.RealVector ufv,
                                org.apache.commons.math3.linear.RealVector ifv,
                                FeatureInfo.Builder fib)

Add a feature’s summary to the feature info builder.

Parameters:

ufv - The user values.

ifv - The item values.

fib - The feature info builder.