/**
* 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;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.Preference;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.common.iterator.FixedSizeSamplingIterator;
import java.util.Iterator;
/**
* <p>returns all items that have not been rated by the user and that were preferred by another user
* that has preferred at least one item that the current user has preferred too</p>
*
* <p>this strategy uses sampling in a way that only a certain amount of preferences per item is considered
* <pre>
* max(defaultMaxPrefsPerItemConsidered, userItemCountFactor * log(max(N_users, N_items)))
* </pre></p>
*/
public class SamplingCandidateItemsStrategy extends AbstractCandidateItemsStrategy {
private final int defaultMaxPrefsPerItemConsidered;
private final int userItemCountMultiplier;
/**
* uses defaultMaxPrefsPerItemConsidered = 100 and userItemCountMultiplier = 20 as default values
*
* @see SamplingCandidateItemsStrategy#SamplingCandidateItemsStrategy(int, int)
*/
public SamplingCandidateItemsStrategy() {
this(100, 20);
}
/**
* <p>the maximum number of prefs considered per item will be computed like this:
* <pre>
* max(defaultMaxPrefsPerItemConsidered, userItemCountFactor * log(max(N_users, N_items)))
* </pre>
* </p>
*
* @param defaultMaxPrefsPerItemConsidered
* @param userItemCountMultiplier
*/
public SamplingCandidateItemsStrategy(int defaultMaxPrefsPerItemConsidered, int userItemCountMultiplier) {
this.defaultMaxPrefsPerItemConsidered = defaultMaxPrefsPerItemConsidered;
this.userItemCountMultiplier = userItemCountMultiplier;
}
@Override
protected FastIDSet doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel) throws TasteException {
int maxPrefsPerItemConsidered = (int) Math.max(defaultMaxPrefsPerItemConsidered,
userItemCountMultiplier * Math.log(Math.max(dataModel.getNumUsers(), dataModel.getNumItems())));
FastIDSet possibleItemsIDs = new FastIDSet();
for (long itemID : preferredItemIDs) {
PreferenceArray prefs = dataModel.getPreferencesForItem(itemID);
int prefsConsidered = Math.min(prefs.length(), maxPrefsPerItemConsidered);
Iterator<Preference> sampledPrefs = new FixedSizeSamplingIterator(prefsConsidered, prefs.iterator());
while (sampledPrefs.hasNext()) {
possibleItemsIDs.addAll(dataModel.getItemIDsFromUser(sampledPrefs.next().getUserID()));
}
}
possibleItemsIDs.removeAll(preferredItemIDs);
return possibleItemsIDs;
}
}