org.codelibs.elasticsearch.taste.recommender.GenericBooleanPrefItemBasedRecommender Maven / Gradle / Ivy

Go to download
/**
 * 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.codelibs.elasticsearch.taste.recommender;

import org.codelibs.elasticsearch.taste.model.DataModel;
import org.codelibs.elasticsearch.taste.model.PreferenceArray;
import org.codelibs.elasticsearch.taste.similarity.ItemSimilarity;

/**
 * A variant on {@link GenericItemBasedRecommender} which is appropriate for use when no notion of preference
 * value exists in the data.
 *
 * @see org.codelibs.elasticsearch.taste.recommender.GenericBooleanPrefUserBasedRecommender
 */
public final class GenericBooleanPrefItemBasedRecommender extends
        GenericItemBasedRecommender {

    public GenericBooleanPrefItemBasedRecommender(final DataModel dataModel,
            final ItemSimilarity similarity) {
        super(dataModel, similarity);
    }

    public GenericBooleanPrefItemBasedRecommender(
            final DataModel dataModel,
            final ItemSimilarity similarity,
            final CandidateItemsStrategy candidateItemsStrategy,
            final MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy) {
        super(dataModel, similarity, candidateItemsStrategy,
                mostSimilarItemsCandidateItemsStrategy);
    }

    /**
     * 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.
     */
    @Override
    protected float doEstimatePreference(final long userID,
            final PreferenceArray preferencesFromUser, final long itemID) {
        final double[] similarities = getSimilarity().itemSimilarities(itemID,
                preferencesFromUser.getIDs());
        boolean foundAPref = false;
        double totalSimilarity = 0.0;
        for (final double theSimilarity : similarities) {
            if (!Double.isNaN(theSimilarity)) {
                foundAPref = true;
                totalSimilarity += theSimilarity;
            }
        }
        return foundAPref ? (float) totalSimilarity : Float.NaN;
    }

    @Override
    public String toString() {
        return "GenericBooleanPrefItemBasedRecommender";
    }

}