org.grouplens.lenskit.mf.svd

Class BiasedMFItemScorer

java.lang.Object

org.grouplens.lenskit.basic.AbstractItemScorer

org.grouplens.lenskit.mf.svd.BiasedMFItemScorer

All Implemented Interfaces:

ItemScorer

public class BiasedMFItemScorer
extends AbstractItemScorer

Item scorer using biased matrix factorization. This implements SVD-style item scorers.

Since:

2.1

Constructor Summary

Constructors

Constructor and Description
BiasedMFItemScorer(MFModel mod, BiasedMFKernel kern, ItemScorer bl) Create a new biased MF item scorer.

Method Summary

Modifier and Type	Method and Description
protected mikera.vectorz.AVector	getUserPreferenceVector(long user) Get a user's preference vector.
void	score(long user, MutableSparseVector scores) Score items in a vector.

Methods inherited from class org.grouplens.lenskit.basic.AbstractItemScorer

score, score

Methods inherited from class java.lang.Object

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

Constructor Detail

BiasedMFItemScorer

@Inject
public BiasedMFItemScorer(MFModel mod,
                                  BiasedMFKernel kern,
                                  @BaselineScorer
                                  ItemScorer bl)

Create a new biased MF item scorer.

Parameters:

mod - The model (factorized matrix)

kern - The kernel function to compute scores.

bl - The baseline scorer (used to compute biases).

Method Detail

getUserPreferenceVector

@Nullable
protected mikera.vectorz.AVector getUserPreferenceVector(long user)

Get a user's preference vector.

Parameters:

user - The user ID.

Returns:

The user's preference vector, or null if no preferences are available for the user.

score

public void score(long user,
                  @Nonnull
                  MutableSparseVector scores)

Description copied from interface: ItemScorer

Score items in a vector. The key domain of the provided vector is the items to score, and the score method sets the values for each item to its score (or unsets it, if no score can be provided). The previous values are discarded.

Parameters:

user - The user ID.

scores - The score vector.