[MAHOUT-359] org.apache.mahout.cf.taste.hadoop.item.RecommenderJob for Boolean recommendation

• Type: Bug
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 0.4
• Fix Version/s: 0.4
• Assignee: Sean R. Owen
• Reporter: Han Hui Wen
• Created: 02/Apr/10 08:42
• Updated: 31/Jan/24 22:11
• Resolved: 27/Apr/10 08:52

Description

in some case there has no preference value in the input data ,the preference value is set to zero,then

RecommenderMapper.class

@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 .