LogLikelihoodSimilarity.java

/**
 * 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.similarity;

import java.util.Collection;

import org.apache.mahout.math.stats.LogLikelihood;
import org.codelibs.elasticsearch.taste.common.FastIDSet;
import org.codelibs.elasticsearch.taste.common.RefreshHelper;
import org.codelibs.elasticsearch.taste.common.Refreshable;
import org.codelibs.elasticsearch.taste.model.DataModel;

/**
 * See <a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5962">
 * http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.5962</a> and
 * <a href="http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html">
 * http://tdunning.blogspot.com/2008/03/surprise-and-coincidence.html</a>.
 */
public final class LogLikelihoodSimilarity extends AbstractItemSimilarity
        implements UserSimilarity {

    public LogLikelihoodSimilarity(final DataModel dataModel) {
        super(dataModel);
    }

    /**
     * @throws UnsupportedOperationException
     */
    @Override
    public void setPreferenceInferrer(final PreferenceInferrer inferrer) {
        throw new UnsupportedOperationException();
    }

    @Override
    public double userSimilarity(final long userID1, final long userID2) {

        final DataModel dataModel = getDataModel();
        final FastIDSet prefs1 = dataModel.getItemIDsFromUser(userID1);
        final FastIDSet prefs2 = dataModel.getItemIDsFromUser(userID2);

        final long prefs1Size = prefs1.size();
        final long prefs2Size = prefs2.size();
        final long intersectionSize = prefs1Size < prefs2Size ? prefs2
                .intersectionSize(prefs1) : prefs1.intersectionSize(prefs2);
        if (intersectionSize == 0) {
            return Double.NaN;
        }
        final long numItems = dataModel.getNumItems();
        final double logLikelihood = LogLikelihood.logLikelihoodRatio(
                intersectionSize, prefs2Size - intersectionSize, prefs1Size
                        - intersectionSize, numItems - prefs1Size - prefs2Size
                        + intersectionSize);
        return 1.0 - 1.0 / (1.0 + logLikelihood);
    }

    @Override
    public double itemSimilarity(final long itemID1, final long itemID2) {
        final DataModel dataModel = getDataModel();
        final long preferring1 = dataModel
                .getNumUsersWithPreferenceFor(itemID1);
        final long numUsers = dataModel.getNumUsers();
        return doItemSimilarity(itemID1, itemID2, preferring1, numUsers);
    }

    @Override
    public double[] itemSimilarities(final long itemID1, final long[] itemID2s) {
        final DataModel dataModel = getDataModel();
        final long preferring1 = dataModel
                .getNumUsersWithPreferenceFor(itemID1);
        final long numUsers = dataModel.getNumUsers();
        final int length = itemID2s.length;
        final double[] result = new double[length];
        for (int i = 0; i < length; i++) {
            result[i] = doItemSimilarity(itemID1, itemID2s[i], preferring1,
                    numUsers);
        }
        return result;
    }

    private double doItemSimilarity(final long itemID1, final long itemID2,
            final long preferring1, final long numUsers) {
        final DataModel dataModel = getDataModel();
        final long preferring1and2 = dataModel.getNumUsersWithPreferenceFor(
                itemID1, itemID2);
        if (preferring1and2 == 0) {
            return Double.NaN;
        }
        final long preferring2 = dataModel
                .getNumUsersWithPreferenceFor(itemID2);
        final double logLikelihood = LogLikelihood.logLikelihoodRatio(
                preferring1and2, preferring2 - preferring1and2, preferring1
                        - preferring1and2, numUsers - preferring1 - preferring2
                        + preferring1and2);
        return 1.0 - 1.0 / (1.0 + logLikelihood);
    }

    @Override
    public void refresh(Collection<Refreshable> alreadyRefreshed) {
        alreadyRefreshed = RefreshHelper.buildRefreshed(alreadyRefreshed);
        RefreshHelper.maybeRefresh(alreadyRefreshed, getDataModel());
    }

    @Override
    public String toString() {
        return "LogLikelihoodSimilarity[dataModel:" + getDataModel() + ']';
    }

}