/*
* 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.slopeone;
import it.unimi.dsi.fastutil.longs.LongIterator;
import org.grouplens.lenskit.data.history.History;
import org.grouplens.lenskit.data.history.UserHistory;
import org.grouplens.lenskit.data.dao.UserEventDAO;
import org.grouplens.lenskit.data.event.Rating;
import org.grouplens.lenskit.data.history.RatingVectorUserHistorySummarizer;
import org.grouplens.lenskit.data.pref.PreferenceDomain;
import org.grouplens.lenskit.vectors.MutableSparseVector;
import org.grouplens.lenskit.vectors.SparseVector;
import org.grouplens.lenskit.vectors.VectorEntry;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import javax.inject.Inject;
/**
* An {@link org.grouplens.lenskit.ItemScorer} that implements a weighted Slope One algorithm.
*/
public class WeightedSlopeOneItemScorer extends SlopeOneItemScorer {
@Inject
public WeightedSlopeOneItemScorer(UserEventDAO dao, SlopeOneModel model,
@Nullable PreferenceDomain dom) {
super(dao, model, dom);
}
@Override
public void score(long uid, @Nonnull MutableSparseVector scores) {
UserHistory<Rating> history = dao.getEventsForUser(uid, Rating.class);
if (history == null) {
history = History.forUser(uid);
}
SparseVector ratings = RatingVectorUserHistorySummarizer.makeRatingVector(history);
for (VectorEntry e : scores.view(VectorEntry.State.EITHER)) {
final long predicteeItem = e.getKey();
if (!ratings.containsKey(predicteeItem)) {
double total = 0;
int nusers = 0;
LongIterator ratingIter = ratings.keySet().iterator();
while (ratingIter.hasNext()) {
long currentItem = ratingIter.nextLong();
double currentDev = model.getDeviation(predicteeItem, currentItem);
if (!Double.isNaN(currentDev)) {
int weight = model.getCoratings(predicteeItem, currentItem);
total += (currentDev + ratings.get(currentItem)) * weight;
nusers += weight;
}
}
if (nusers == 0) {
scores.unset(e);
} else {
double predValue = total / nusers;
if (domain != null) {
predValue = domain.clampValue(predValue);
}
scores.set(e, predValue);
}
}
}
}
}