org.apache.mahout.cf.taste.hadoop.item.RecommenderJob for Boolean recommendation
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Key: MAHOUT-359
URL: https://issues.apache.org/jira/browse/MAHOUT-359
Project: Mahout
Issue Type: Bug
Components: Collaborative Filtering
Affects Versions: 0.4
Reporter: Hui Wen Han
in some case there has no preference value in the input data ,the preference
value is set to zero,then
RecommenderMapper.class
line 119:
@Override
public void map(LongWritable userID,
VectorWritable vectorWritable,
OutputCollector<LongWritable,RecommendedItemsWritable> output,
Reporter reporter) throws IOException {
if ((usersToRecommendFor != null) &&
!usersToRecommendFor.contains(userID.get())) {
return;
}
Vector userVector = vectorWritable.get();
Iterator<Vector.Element> userVectorIterator = userVector.iterateNonZero();
Vector recommendationVector = new
RandomAccessSparseVector(Integer.MAX_VALUE, 1000);
while (userVectorIterator.hasNext()) {
Vector.Element element = userVectorIterator.next();
int index = element.index();
double value = element.get(); //here will get 0.0 for Boolean
recommendation
Vector columnVector;
try {
columnVector = cooccurrenceColumnCache.get(new IntWritable(index));
} catch (TasteException te) {
if (te.getCause() instanceof IOException) {
throw (IOException) te.getCause();
} else {
throw new IOException(te.getCause());
}
}
if (columnVector != null) {
columnVector.times(value).addTo(recommendationVector); //here will set
all score value to zero for Boolean recommendation
}
}
Queue<RecommendedItem> topItems = new
PriorityQueue<RecommendedItem>(recommendationsPerUser + 1,
Collections.reverseOrder());
Iterator<Vector.Element> recommendationVectorIterator =
recommendationVector.iterateNonZero();
LongWritable itemID = new LongWritable();
while (recommendationVectorIterator.hasNext()) {
Vector.Element element = recommendationVectorIterator.next();
int index = element.index();
if (userVector.get(index) == 0.0) {
if (topItems.size() < recommendationsPerUser) {
indexItemIDMap.get(new IntWritable(index), itemID);
topItems.add(new GenericRecommendedItem(itemID.get(), (float)
element.get()));
} else if (element.get() > topItems.peek().getValue()) {
indexItemIDMap.get(new IntWritable(index), itemID);
topItems.add(new GenericRecommendedItem(itemID.get(), (float)
element.get()));
topItems.poll();
}
}
}
List<RecommendedItem> recommendations = new
ArrayList<RecommendedItem>(topItems.size());
recommendations.addAll(topItems);
Collections.sort(recommendations);
output.collect(userID, new RecommendedItemsWritable(recommendations));
}
so maybe we need a option to distinguish boolean recommendation and slope one
recommendation.
in ToUserVectorReducer.class
here no need findTopNPrefsCutoff,maybe take all item.
it's just my thinking ,maybe item is used for slope one only .
:)
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