SVDItemScorer.java

package edu.umn.cs.recsys.svd;

import org.apache.commons.math3.linear.RealMatrix;
import org.grouplens.lenskit.ItemScorer;
import org.grouplens.lenskit.baseline.BaselineScorer;
import org.grouplens.lenskit.basic.AbstractItemScorer;
import org.grouplens.lenskit.data.dao.UserEventDAO;
import org.grouplens.lenskit.data.event.Rating;
import org.grouplens.lenskit.data.history.History;
import org.grouplens.lenskit.data.history.RatingVectorUserHistorySummarizer;
import org.grouplens.lenskit.data.history.UserHistory;
import org.grouplens.lenskit.vectors.MutableSparseVector;
import org.grouplens.lenskit.vectors.SparseVector;
import org.grouplens.lenskit.vectors.VectorEntry;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import javax.annotation.Nonnull;
import javax.inject.Inject;

/**
 * SVD-based item scorer.
 */
public class SVDItemScorer extends AbstractItemScorer {
	private static final Logger logger = LoggerFactory
			.getLogger(SVDItemScorer.class);
	private final SVDModel model;
	private final ItemScorer baselineScorer;
	private final UserEventDAO userEvents;

	/**
	 * Construct an SVD item scorer using a model.
	 * 
	 * @param m
	 *            The model to use when generating scores.
	 * @param uedao
	 *            A DAO to get user rating profiles.
	 * @param baseline
	 *            The baseline scorer (providing means).
	 */
	@Inject
	public SVDItemScorer(SVDModel m, UserEventDAO uedao,
			@BaselineScorer ItemScorer baseline) {
		model = m;
		baselineScorer = baseline;
		userEvents = uedao;
	}

	/**
	 * 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.
	 * 
	 * @param user
	 *            The user ID.
	 * @param scores
	 *            The score vector.
	 */
	@Override
	public void score(long user, @Nonnull MutableSparseVector scores) {
		// P = b + U.S.Vt
		if (model.getUserVector(user) == null) {
			scores.clear();
		} else {
			RealMatrix U = model.getUserVector(user);
			RealMatrix S = model.getFeatureWeights();

			for (VectorEntry e : scores.fast(VectorEntry.State.EITHER)) {
				long item = e.getKey();
				RealMatrix V = model.getItemVector(item);
				scores.set(item,
						baselineScorer.score(user, item)
								+ (U.multiply(S)).multiply(V.transpose())
										.getEntry(0, 0));
			}
		}
	}

	/**
	 * Get a user's ratings.
	 * 
	 * @param user
	 *            The user ID.
	 * @return The ratings to retrieve.
	 */
	private SparseVector getUserRatingVector(long user) {
		UserHistory<Rating> history = userEvents.getEventsForUser(user,
				Rating.class);
		if (history == null) {
			history = History.forUser(user);
		}

		return RatingVectorUserHistorySummarizer.makeRatingVector(history);
	}
}