/**
* 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.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
/**
* A variant on {@link GenericUserBasedRecommender} which is appropriate for use when no notion of preference
* value exists in the data.
*/
public final class GenericBooleanPrefUserBasedRecommender extends GenericUserBasedRecommender {
public GenericBooleanPrefUserBasedRecommender(DataModel dataModel,
UserNeighborhood neighborhood,
UserSimilarity similarity) {
super(dataModel, neighborhood, similarity);
}
/**
* This computation is in a technical sense, wrong, since in the domain of "boolean preference users" where
* all preference values are 1, this method should only ever return 1.0 or NaN. This isn't terribly useful
* however since it means results can't be ranked by preference value (all are 1). So instead this returns a
* sum of similarities to any other user in the neighborhood who has also rated the item.
*/
@Override
protected float doEstimatePreference(long theUserID, long[] theNeighborhood, long itemID) throws TasteException {
if (theNeighborhood.length == 0) {
return Float.NaN;
}
DataModel dataModel = getDataModel();
UserSimilarity similarity = getSimilarity();
float totalSimilarity = 0.0f;
boolean foundAPref = false;
for (long userID : theNeighborhood) {
// See GenericItemBasedRecommender.doEstimatePreference() too
if (userID != theUserID && dataModel.getPreferenceValue(userID, itemID) != null) {
foundAPref = true;
totalSimilarity += (float) similarity.userSimilarity(theUserID, userID);
}
}
return foundAPref ? totalSimilarity : Float.NaN;
}
@Override
protected FastIDSet getAllOtherItems(long[] theNeighborhood, long theUserID) throws TasteException {
DataModel dataModel = getDataModel();
FastIDSet possibleItemIDs = new FastIDSet();
for (long userID : theNeighborhood) {
possibleItemIDs.addAll(dataModel.getItemIDsFromUser(userID));
}
possibleItemIDs.removeAll(dataModel.getItemIDsFromUser(theUserID));
return possibleItemIDs;
}
@Override
public String toString() {
return "GenericBooleanPrefUserBasedRecommender";
}
}