/*
* LensKit, an open source recommender systems toolkit.
* Copyright 2010-2014 LensKit Contributors. See CONTRIBUTORS.md.
* Work on LensKit has been funded by the National Science Foundation under
* grants IIS 05-34939, 08-08692, 08-12148, and 10-17697.
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation; either version 2.1 of the
* License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
* details.
*
* You should have received a copy of the GNU General Public License along with
* this program; if not, write to the Free Software Foundation, Inc., 51
* Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/
package org.grouplens.lenskit.knn.user;
import com.google.common.base.Preconditions;
import com.google.common.collect.AbstractIterator;
import it.unimi.dsi.fastutil.longs.LongCollection;
import it.unimi.dsi.fastutil.longs.LongIterator;
import it.unimi.dsi.fastutil.longs.LongOpenHashSet;
import it.unimi.dsi.fastutil.longs.LongSet;
import org.grouplens.lenskit.data.dao.ItemEventDAO;
import org.grouplens.lenskit.data.dao.UserEventDAO;
import org.grouplens.lenskit.data.event.Event;
import org.grouplens.lenskit.data.event.Rating;
import org.grouplens.lenskit.data.event.Ratings;
import org.grouplens.lenskit.data.history.RatingVectorUserHistorySummarizer;
import org.grouplens.lenskit.data.history.UserHistory;
import org.grouplens.lenskit.transform.normalize.UserVectorNormalizer;
import org.grouplens.lenskit.transform.threshold.Threshold;
import org.grouplens.lenskit.vectors.ImmutableSparseVector;
import org.grouplens.lenskit.vectors.MutableSparseVector;
import org.grouplens.lenskit.vectors.SparseVector;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.inject.Inject;
import java.util.Iterator;
import java.util.List;
/**
* Neighborhood finder that does a fresh search over the data source ever time.
*
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class LiveNeighborFinder implements NeighborFinder {
private static final Logger logger = LoggerFactory.getLogger(LiveNeighborFinder.class);
private final UserSimilarity similarity;
private final UserEventDAO userDAO;
private final ItemEventDAO itemDAO;
private final UserVectorNormalizer normalizer;
private final Threshold threshold;
/**
* Construct a new user neighborhood finder.
*
* @param udao The user-event DAO.
* @param idao The item-event DAO.
* @param sim The similarity function to use.
* @param norm The normalizer for user rating/preference vectors.
* @param thresh The threshold for user similarities.
*/
@Inject
public LiveNeighborFinder(UserEventDAO udao, ItemEventDAO idao,
UserSimilarity sim,
UserVectorNormalizer norm,
@UserSimilarityThreshold Threshold thresh) {
similarity = sim;
normalizer = norm;
userDAO = udao;
itemDAO = idao;
threshold = thresh;
Preconditions.checkArgument(sim.isSparse(), "user similarity function is not sparse");
}
@Override
public Iterable<Neighbor> getCandidateNeighbors(UserHistory<? extends Event> user, LongSet items) {
final long uid = user.getUserId();
SparseVector urs = RatingVectorUserHistorySummarizer.makeRatingVector(user);
final ImmutableSparseVector nratings = normalizer.normalize(user.getUserId(), urs, null)
.freeze();
final LongSet candidates = findCandidateNeighbors(uid, nratings, items);
logger.debug("found {} candidate neighbors for {}", candidates.size(), uid);
return new Iterable<Neighbor>() {
@Override
public Iterator<Neighbor> iterator() {
return new NeighborIterator(uid, nratings, candidates);
}
};
}
/**
* Get the IDs of the candidate neighbors for a user.
* @param user The user.
* @param uvec The user's normalized preference vector.
* @param itemSet The set of target items.
* @return The set of IDs of candidate neighbors.
*/
private LongSet findCandidateNeighbors(long user, SparseVector uvec, LongCollection itemSet) {
LongSet users = new LongOpenHashSet(100);
LongSet userItems = uvec.keySet();
LongIterator items;
if (userItems.size() < itemSet.size()) {
items = userItems.iterator();
} else {
items = itemSet.iterator();
}
while (items.hasNext()) {
LongSet iusers = itemDAO.getUsersForItem(items.nextLong());
if (iusers != null) {
users.addAll(iusers);
}
}
users.remove(user);
return users;
}
/**
* Check if a similarity is acceptable.
*
* @param sim The similarity to check.
* @return {@code false} if the similarity is NaN, infinite, or rejected by the threshold;
* {@code true} otherwise.
*/
private boolean acceptSimilarity(double sim) {
return !Double.isNaN(sim) && !Double.isInfinite(sim) && threshold.retain(sim);
}
private MutableSparseVector getUserRatingVector(long user) {
List<Rating> ratings = userDAO.getEventsForUser(user, Rating.class);
if (ratings == null){
return null;
}
return Ratings.userRatingVector(ratings);
}
private class NeighborIterator extends AbstractIterator<Neighbor> {
private final long user;
private final SparseVector userVector;
private final LongIterator neighborIter;
public NeighborIterator(long uid, SparseVector uvec, LongSet nbrs) {
user = uid;
userVector = uvec;
neighborIter = nbrs.iterator();
}
@Override
protected Neighbor computeNext() {
while (neighborIter.hasNext()) {
final long neighbor = neighborIter.nextLong();
MutableSparseVector nbrRatings = getUserRatingVector(neighbor);
if (nbrRatings != null) {
ImmutableSparseVector rawRatings = nbrRatings.immutable();
normalizer.normalize(neighbor, rawRatings, nbrRatings);
final double sim = similarity.similarity(user, userVector, neighbor, nbrRatings);
if (acceptSimilarity(sim)) {
// we have found a neighbor
return new Neighbor(neighbor, rawRatings, sim);
}
}
}
// no neighbor found, done
return endOfData();
}
}
}