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

/**
 * @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);

        for (VectorEntry e: scores.fast(VectorEntry.State.EITHER)) {
            long item = e.getKey();
            List<ScoredId> neighbors = model.getNeighbors(item);
			int nghbrCount = 0;
			double numerator = 0, denominator = 0;

			for(ScoredId thisNghbr : neighbors){
				if(nghbrCount < neighborhoodSize){
					if (!ratings.containsKey(thisNghbr.getId())) continue;
					double thisItemRating = ratings.get(thisNghbr.getId());
					double thisItemSimilarity = thisNghbr.getScore();

					numerator += thisItemRating * thisItemSimilarity;
					denominator += thisItemSimilarity;
					nghbrCount++;
				}
			}
			scores.set(item, numerator/denominator);
        }
    }

    /**
     * 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);
    }
}