SimpleItemItemScorer.java

package edu.umn.cs.recsys.ii;

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.knn.NeighborhoodSize;
import org.grouplens.lenskit.scored.ScoredId;
import org.grouplens.lenskit.vectors.MutableSparseVector;
import org.grouplens.lenskit.vectors.SparseVector;
import org.grouplens.lenskit.vectors.VectorEntry;

import it.unimi.dsi.fastutil.longs.LongSortedSet;

import javax.annotation.Nonnull;
import javax.inject.Inject;
import java.util.List;

/**
 * @author <a href="http://www.grouplens.org">GroupLens Research</a>
 */
public class SimpleItemItemScorer extends AbstractItemScorer {
	private final SimpleItemItemModel model;
	private final UserEventDAO userEvents;
	private final int neighborhoodSize;

	@Inject
	public SimpleItemItemScorer(SimpleItemItemModel m, UserEventDAO dao,
			@NeighborhoodSize int nnbrs) {
		model = m;
		userEvents = dao;
		neighborhoodSize = nnbrs;
	}

	/**
	 * Score items for a user.
	 * @param user The user ID.
	 * @param scores The score vector.  Its key domain is the items to score, and the scores
	 *               (rating predictions) should be written back to this vector.
	 */
	@Override
	public void score(long user, @Nonnull MutableSparseVector scores) {
		SparseVector ratings = getUserRatingVector(user);
		LongSortedSet userRatedItems = ratings.keySet();

		for (VectorEntry e: scores.fast(VectorEntry.State.EITHER)) {
			//  Score this item and save the score into scores
			long item = e.getKey();
			List<ScoredId> neighbors = model.getNeighbors(item);
			
			double upperPart = 0.0, downPart = 0.0;
			
			int neighCount = 0;
			
			for (ScoredId scorePair : neighbors) {
				long neighID = scorePair.getId();
				double similarity = scorePair.getScore();

				// only considers rated by the user
				if(userRatedItems.contains(neighID)){
					// only iterates over neighborhoodSize neighbors
					if(neighborhoodSize == neighCount++) break;
						
					double rating = ratings.get(neighID);
					upperPart += rating * similarity;
					downPart += similarity;
				}
			}
			
			double score = upperPart / downPart;
			scores.set(e, score);
		}
	}

	/**
	 * 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);
	}
}