/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.cf.taste.impl.recommender.slopeone;
import java.util.Collection;
import java.util.List;
import org.apache.mahout.cf.taste.common.NoSuchUserException;
import org.apache.mahout.cf.taste.common.Refreshable;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.common.Weighting;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.impl.common.RefreshHelper;
import org.apache.mahout.cf.taste.impl.common.RunningAverage;
import org.apache.mahout.cf.taste.impl.common.RunningAverageAndStdDev;
import org.apache.mahout.cf.taste.impl.recommender.AbstractRecommender;
import org.apache.mahout.cf.taste.impl.recommender.TopItems;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.IDRescorer;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.slopeone.DiffStorage;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.google.common.base.Preconditions;
/**
* <p>
* A basic "slope one" recommender. (See an <a href="http://www.daniel-lemire.com/fr/abstracts/SDM2005.html">
* excellent summary here</a> for example.) This {@link org.apache.mahout.cf.taste.recommender.Recommender} is
* especially suitable when user preferences are updating frequently as it can incorporate this information
* without expensive recomputation.
* </p>
*
* <p>
* This implementation can also be used as a "weighted slope one" recommender.
* </p>
*/
public final class SlopeOneRecommender extends AbstractRecommender {
private static final Logger log = LoggerFactory.getLogger(SlopeOneRecommender.class);
private final boolean weighted;
private final boolean stdDevWeighted;
private final DiffStorage diffStorage;
/**
* <p>
* Creates a default (weighted) based on the given {@link DataModel}.
* </p>
*/
public SlopeOneRecommender(DataModel dataModel) throws TasteException {
this(dataModel,
Weighting.WEIGHTED,
Weighting.WEIGHTED,
new MemoryDiffStorage(dataModel, Weighting.WEIGHTED, Long.MAX_VALUE));
}
/**
* <p>
* Creates a based on the given {@link DataModel}.
* </p>
*
* <p>
* If {@code weighted} is set, acts as a weighted slope one recommender. This implementation also
* includes an experimental "standard deviation" weighting which weights item-item ratings diffs with lower
* standard deviation more highly, on the theory that they are more reliable.
* </p>
*
* @param weighting
* if {@link Weighting#WEIGHTED}, acts as a weighted slope one recommender
* @param stdDevWeighting
* use optional standard deviation weighting of diffs
* @throws IllegalArgumentException
* if {@code diffStorage} is null, or stdDevWeighted is set when weighted is not set
*/
public SlopeOneRecommender(DataModel dataModel,
Weighting weighting,
Weighting stdDevWeighting,
DiffStorage diffStorage) {
super(dataModel);
Preconditions.checkArgument(stdDevWeighting != Weighting.WEIGHTED || weighting != Weighting.UNWEIGHTED,
"weighted required when stdDevWeighted is set");
Preconditions.checkArgument(diffStorage != null, "diffStorage is null");
this.weighted = weighting == Weighting.WEIGHTED;
this.stdDevWeighted = stdDevWeighting == Weighting.WEIGHTED;
this.diffStorage = diffStorage;
}
@Override
public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
log.debug("Recommending items for user ID '{}'", userID);
FastIDSet possibleItemIDs = diffStorage.getRecommendableItemIDs(userID);
TopItems.Estimator<Long> estimator = new Estimator(userID);
List<RecommendedItem> topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer,
estimator);
log.debug("Recommendations are: {}", topItems);
return topItems;
}
@Override
public float estimatePreference(long userID, long itemID) throws TasteException {
DataModel model = getDataModel();
Float actualPref = model.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
return doEstimatePreference(userID, itemID);
}
private float doEstimatePreference(long userID, long itemID) throws TasteException {
double count = 0.0;
double totalPreference = 0.0;
PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
RunningAverage[] averages = diffStorage.getDiffs(userID, itemID, prefs);
int size = prefs.length();
for (int i = 0; i < size; i++) {
RunningAverage averageDiff = averages[i];
if (averageDiff != null) {
double averageDiffValue = averageDiff.getAverage();
if (weighted) {
double weight = averageDiff.getCount();
if (stdDevWeighted) {
double stdev = ((RunningAverageAndStdDev) averageDiff).getStandardDeviation();
if (!Double.isNaN(stdev)) {
weight /= 1.0 + stdev;
}
// If stdev is NaN, then it is because count is 1. Because we're weighting by count,
// the weight is already relatively low. We effectively assume stdev is 0.0 here and
// that is reasonable enough. Otherwise, dividing by NaN would yield a weight of NaN
// and disqualify this pref entirely
// (Thanks Daemmon)
}
totalPreference += weight * (prefs.getValue(i) + averageDiffValue);
count += weight;
} else {
totalPreference += prefs.getValue(i) + averageDiffValue;
count += 1.0;
}
}
}
if (count <= 0.0) {
RunningAverage itemAverage = diffStorage.getAverageItemPref(itemID);
return itemAverage == null ? Float.NaN : (float) itemAverage.getAverage();
} else {
return (float) (totalPreference / count);
}
}
@Override
public void setPreference(long userID, long itemID, float value) throws TasteException {
DataModel dataModel = getDataModel();
Float oldPref;
try {
oldPref = dataModel.getPreferenceValue(userID, itemID);
} catch (NoSuchUserException nsee) {
oldPref = null;
}
super.setPreference(userID, itemID, value);
if (oldPref == null) {
// Add new preference
diffStorage.addItemPref(userID, itemID, value);
} else {
// Update preference
diffStorage.updateItemPref(itemID, value - oldPref);
}
}
@Override
public void removePreference(long userID, long itemID) throws TasteException {
DataModel dataModel = getDataModel();
Float oldPref = dataModel.getPreferenceValue(userID, itemID);
super.removePreference(userID, itemID);
if (oldPref != null) {
diffStorage.removeItemPref(userID, itemID, oldPref);
}
}
@Override
public void refresh(Collection<Refreshable> alreadyRefreshed) {
alreadyRefreshed = RefreshHelper.buildRefreshed(alreadyRefreshed);
RefreshHelper.maybeRefresh(alreadyRefreshed, diffStorage);
}
@Override
public String toString() {
return "SlopeOneRecommender[weighted:" + weighted + ", stdDevWeighted:" + stdDevWeighted
+ ", diffStorage:" + diffStorage + ']';
}
private final class Estimator implements TopItems.Estimator<Long> {
private final long userID;
private Estimator(long userID) {
this.userID = userID;
}
@Override
public double estimate(Long itemID) throws TasteException {
return doEstimatePreference(userID, itemID);
}
}
}