org.apache.mahout.cf.taste.hadoop.item.AggregateAndRecommendReducer Maven / Gradle / Ivy
/*
* 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.hadoop.item;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.mahout.cf.taste.hadoop.MutableRecommendedItem;
import org.apache.mahout.cf.taste.hadoop.RecommendedItemsWritable;
import org.apache.mahout.cf.taste.hadoop.TasteHadoopUtils;
import org.apache.mahout.cf.taste.hadoop.TopItemsQueue;
import org.apache.mahout.cf.taste.impl.common.FastIDSet;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.VarLongWritable;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.Vector.Element;
import org.apache.mahout.math.function.Functions;
import org.apache.mahout.math.map.OpenIntLongHashMap;
import java.io.IOException;
import java.util.Iterator;
import java.util.List;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* computes prediction values for each user
*
*
* u = a user
* i = an item not yet rated by u
* N = all items similar to i (where similarity is usually computed by pairwisely comparing the item-vectors
* of the user-item matrix)
*
* Prediction(u,i) = sum(all n from N: similarity(i,n) * rating(u,n)) / sum(all n from N: abs(similarity(i,n)))
*
*/
public final class AggregateAndRecommendReducer extends
Reducer {
private static final Logger log = LoggerFactory.getLogger(AggregateAndRecommendReducer.class);
static final String ITEMID_INDEX_PATH = "itemIDIndexPath";
static final String NUM_RECOMMENDATIONS = "numRecommendations";
static final int DEFAULT_NUM_RECOMMENDATIONS = 10;
static final String ITEMS_FILE = "itemsFile";
private boolean booleanData;
private int recommendationsPerUser;
private IDReader idReader;
private FastIDSet itemsToRecommendFor;
private OpenIntLongHashMap indexItemIDMap;
private final RecommendedItemsWritable recommendedItems = new RecommendedItemsWritable();
private static final float BOOLEAN_PREF_VALUE = 1.0f;
@Override
protected void setup(Context context) throws IOException {
Configuration conf = context.getConfiguration();
recommendationsPerUser = conf.getInt(NUM_RECOMMENDATIONS, DEFAULT_NUM_RECOMMENDATIONS);
booleanData = conf.getBoolean(RecommenderJob.BOOLEAN_DATA, false);
indexItemIDMap = TasteHadoopUtils.readIDIndexMap(conf.get(ITEMID_INDEX_PATH), conf);
idReader = new IDReader(conf);
idReader.readIDs();
itemsToRecommendFor = idReader.getItemIds();
}
@Override
protected void reduce(VarLongWritable userID,
Iterable values,
Context context) throws IOException, InterruptedException {
if (booleanData) {
reduceBooleanData(userID, values, context);
} else {
reduceNonBooleanData(userID, values, context);
}
}
private void reduceBooleanData(VarLongWritable userID,
Iterable values,
Context context) throws IOException, InterruptedException {
/* having boolean data, each estimated preference can only be 1,
* however we can't use this to rank the recommended items,
* so we use the sum of similarities for that. */
Iterator columns = values.iterator();
Vector predictions = columns.next().getSimilarityColumn();
while (columns.hasNext()) {
predictions.assign(columns.next().getSimilarityColumn(), Functions.PLUS);
}
writeRecommendedItems(userID, predictions, context);
}
private void reduceNonBooleanData(VarLongWritable userID,
Iterable values,
Context context) throws IOException, InterruptedException {
/* each entry here is the sum in the numerator of the prediction formula */
Vector numerators = null;
/* each entry here is the sum in the denominator of the prediction formula */
Vector denominators = null;
/* each entry here is the number of similar items used in the prediction formula */
Vector numberOfSimilarItemsUsed = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
for (PrefAndSimilarityColumnWritable prefAndSimilarityColumn : values) {
Vector simColumn = prefAndSimilarityColumn.getSimilarityColumn();
float prefValue = prefAndSimilarityColumn.getPrefValue();
/* count the number of items used for each prediction */
for (Element e : simColumn.nonZeroes()) {
int itemIDIndex = e.index();
numberOfSimilarItemsUsed.setQuick(itemIDIndex, numberOfSimilarItemsUsed.getQuick(itemIDIndex) + 1);
}
if (denominators == null) {
denominators = simColumn.clone();
} else {
denominators.assign(simColumn, Functions.PLUS_ABS);
}
if (numerators == null) {
numerators = simColumn.clone();
if (prefValue != BOOLEAN_PREF_VALUE) {
numerators.assign(Functions.MULT, prefValue);
}
} else {
if (prefValue != BOOLEAN_PREF_VALUE) {
simColumn.assign(Functions.MULT, prefValue);
}
numerators.assign(simColumn, Functions.PLUS);
}
}
if (numerators == null) {
return;
}
Vector recommendationVector = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
for (Element element : numerators.nonZeroes()) {
int itemIDIndex = element.index();
/* preference estimations must be based on at least 2 datapoints */
if (numberOfSimilarItemsUsed.getQuick(itemIDIndex) > 1) {
/* compute normalized prediction */
double prediction = element.get() / denominators.getQuick(itemIDIndex);
recommendationVector.setQuick(itemIDIndex, prediction);
}
}
writeRecommendedItems(userID, recommendationVector, context);
}
/**
* find the top entries in recommendationVector, map them to the real itemIDs and write back the result
*/
private void writeRecommendedItems(VarLongWritable userID, Vector recommendationVector, Context context)
throws IOException, InterruptedException {
TopItemsQueue topKItems = new TopItemsQueue(recommendationsPerUser);
FastIDSet itemsForUser = null;
if (idReader != null && idReader.isUserItemFilterSpecified()) {
itemsForUser = idReader.getItemsToRecommendForUser(userID.get());
}
for (Element element : recommendationVector.nonZeroes()) {
int index = element.index();
long itemID;
if (indexItemIDMap != null && !indexItemIDMap.isEmpty()) {
itemID = indexItemIDMap.get(index);
} else { // we don't have any mappings, so just use the original
itemID = index;
}
if (shouldIncludeItemIntoRecommendations(itemID, itemsToRecommendFor, itemsForUser)) {
float value = (float) element.get();
if (!Float.isNaN(value)) {
MutableRecommendedItem topItem = topKItems.top();
if (value > topItem.getValue()) {
topItem.set(itemID, value);
topKItems.updateTop();
}
}
}
}
List topItems = topKItems.getTopItems();
if (!topItems.isEmpty()) {
recommendedItems.set(topItems);
context.write(userID, recommendedItems);
}
}
private boolean shouldIncludeItemIntoRecommendations(long itemID, FastIDSet allItemsToRecommendFor,
FastIDSet itemsForUser) {
if (allItemsToRecommendFor == null && itemsForUser == null) {
return true;
} else if (itemsForUser != null) {
return itemsForUser.contains(itemID);
} else {
return allItemsToRecommendFor.contains(itemID);
}
}
}