org.lenskit.basic.TopNItemRecommender Maven / Gradle / Ivy
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/*
* 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.lenskit.basic;
import com.google.common.collect.Ordering;
import it.unimi.dsi.fastutil.longs.*;
import org.lenskit.util.ScoredIdAccumulator;
import org.lenskit.util.TopNScoredIdAccumulator;
import org.lenskit.util.UnlimitedScoredIdAccumulator;
import org.lenskit.api.ItemScorer;
import org.lenskit.api.Result;
import org.lenskit.api.ResultList;
import org.lenskit.api.ResultMap;
import org.lenskit.data.dao.ItemDAO;
import org.lenskit.data.dao.UserEventDAO;
import org.lenskit.data.events.Event;
import org.lenskit.data.history.UserHistory;
import org.lenskit.results.Results;
import org.lenskit.util.collections.LongUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import javax.inject.Inject;
import java.util.Collection;
import java.util.List;
import java.util.Map;
/**
* Recommender that recommends the top N items by a scorer.
* Implements all methods required by {@link AbstractItemRecommender}. The
* default exclude set is all items rated by the user.
*
* Recommendations are returned in descending order of score.
*
* @author GroupLens Research
* @since 1.1
*/
public class TopNItemRecommender extends AbstractItemRecommender {
private static final Logger logger = LoggerFactory.getLogger(TopNItemRecommender.class);
protected final UserEventDAO userEventDAO;
protected final ItemDAO itemDAO;
protected final ItemScorer scorer;
@Inject
public TopNItemRecommender(UserEventDAO uedao, ItemDAO idao, ItemScorer scorer) {
userEventDAO = uedao;
itemDAO = idao;
this.scorer = scorer;
}
public ItemScorer getScorer() {
return scorer;
}
/**
* Implement recommendation by calling {@link ItemScorer#score(long, Collection)} and sorting
* the results by score. This method uses {@link #getDefaultExcludes(long)} to get the default
* exclude set for the user, if none is provided.
*/
@Override
protected List recommend(long user, int n, LongSet candidates, LongSet exclude) {
candidates = getEffectiveCandidates(user, candidates, exclude);
logger.debug("Computing {} recommendations for user {} from {} candidates",
n, user, candidates.size());
Map scores = scorer.score(user, candidates);
ScoredIdAccumulator accum;
if (n >= 0) {
accum = new TopNScoredIdAccumulator(n);
} else {
accum = new UnlimitedScoredIdAccumulator();
}
Long2DoubleFunction map = LongUtils.asLong2DoubleFunction(scores);
LongIterator iter = LongIterators.asLongIterator(scores.keySet().iterator());
while (iter.hasNext()) {
long item = iter.nextLong();
accum.put(item, map.get(item));
}
return accum.finishList();
}
/**
* Implement recommendation by calling {@link ItemScorer#scoreWithDetails(long, Collection)} and sorting
* the results. This method uses {@link #getDefaultExcludes(long)} to get the default
* exclude set for the user, if none is provided.
*/
@Override
protected ResultList recommendWithDetails(long user, int n, LongSet candidates, LongSet exclude) {
candidates = getEffectiveCandidates(user, candidates, exclude);
logger.debug("Computing {} recommendations for user {} from {} candidates",
n, user, candidates.size());
ResultMap scores = scorer.scoreWithDetails(user, candidates);
return getTopNResults(n, scores);
}
private LongSet getEffectiveCandidates(long user, LongSet candidates, LongSet exclude) {
if (candidates == null) {
candidates = getPredictableItems(user);
}
if (exclude == null) {
exclude = getDefaultExcludes(user);
}
logger.debug("computing effective candidates for user {} from {} candidates and {} excluded items",
user, candidates.size(), exclude.size());
if (!exclude.isEmpty()) {
candidates = LongUtils.setDifference(candidates, exclude);
}
return candidates;
}
@Nonnull
private ResultList getTopNResults(int n, Iterable scores) {
Ordering ord = Results.scoreOrder();
List topN;
if (n < 0) {
topN = ord.reverse().immutableSortedCopy(scores);
} else {
topN = ord.greatestOf(scores, n);
}
return Results.newResultList(topN);
}
/**
* Get the default exclude set for a user. The base implementation gets
* all the items they have interacted with.
*
* @param user The user ID.
* @return The set of items to exclude.
*/
protected LongSet getDefaultExcludes(long user) {
return getDefaultExcludes(userEventDAO.getEventsForUser(user));
}
/**
* Get the default exclude set for a user. The base implementation returns
* all the items they have interacted with (from {@link UserHistory#itemSet()}).
*
* @param user The user history.
* @return The set of items to exclude.
*/
protected LongSet getDefaultExcludes(@Nullable UserHistory user) {
if (user == null) {
return LongSets.EMPTY_SET;
} else {
return user.itemSet();
}
}
/**
* Determine the items for which predictions can be made for a certain user.
* This implementation is naive and asks the DAO for all items; subclasses
* should override it with something more efficient if practical.
*
* @param user The user's ID.
* @return All items for which predictions can be generated for the user.
*/
protected LongSet getPredictableItems(long user) {
return itemDAO.getItemIds();
}
}