/*
* 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.basic;
import it.unimi.dsi.fastutil.longs.LongArrayList;
import it.unimi.dsi.fastutil.longs.LongList;
import org.grouplens.lenskit.ItemRecommender;
import org.grouplens.lenskit.ItemScorer;
import org.grouplens.lenskit.scored.ScoredId;
import org.grouplens.lenskit.scored.ScoredIdListBuilder;
import org.grouplens.lenskit.scored.ScoredIds;
import org.grouplens.lenskit.symbols.Symbol;
import org.grouplens.lenskit.vectors.SparseVector;
import javax.annotation.Nullable;
import javax.inject.Inject;
import java.util.Collections;
import java.util.List;
import java.util.Set;
/**
* Item recommender that wraps another item recommender and replaces its scores.
*
* @since 2.1
* @author <a href="http://www.grouplens.org">GroupLens Research</a>
*/
public class RescoringItemRecommender implements ItemRecommender {
public static final Symbol ORIGINAL_SCORE_SYMBOL =
Symbol.of("org.grouplens.lenskit.basic.RescoringItemRecommender.ORIGINAL_SCORE");
private final ItemRecommender delegate;
private final ItemScorer scorer;
/**
* Create a new rescoring item recommender.
* @param rec The recommender.
* @param score The item scorer.
*/
@Inject
public RescoringItemRecommender(ItemRecommender rec, ItemScorer score) {
delegate = rec;
scorer = score;
}
@Override
public List<ScoredId> recommend(long user) {
return rescore(user, delegate.recommend(user));
}
@Override
public List<ScoredId> recommend(long user, int n) {
return rescore(user, delegate.recommend(user, n));
}
@Override
public List<ScoredId> recommend(long user, @Nullable Set<Long> candidates) {
return rescore(user, delegate.recommend(user, candidates));
}
@Override
public List<ScoredId> recommend(long user, int n, @Nullable Set<Long> candidates, @Nullable Set<Long> exclude) {
return rescore(user, delegate.recommend(user, n, candidates, exclude));
}
private List<ScoredId> rescore(long user, List<ScoredId> recs) {
if (recs.isEmpty()) {
return Collections.emptyList();
}
LongList items = new LongArrayList(recs.size());
for (ScoredId sid: recs) {
items.add(sid.getId());
}
SparseVector scores = scorer.score(user, items);
ScoredIdListBuilder builder = ScoredIds.newListBuilder(recs.size());
builder.addChannel(ORIGINAL_SCORE_SYMBOL);
for (ScoredId sid: recs) {
// FIXME Make this not allocate so much memory
builder.add(ScoredIds.copyBuilder(sid)
.setScore(scores.get(sid.getId(), Double.NaN))
.addChannel(ORIGINAL_SCORE_SYMBOL, sid.getScore())
.build());
}
return builder.build();
}
}