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https://issues.apache.org/jira/browse/MAHOUT-359?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12852778#action_12852778
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Sean Owen commented on MAHOUT-359:
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I am not sure I understand the issue yet. This class has nothing to do with
slope one.
Are you looking at this line?
if (userVector.get(index) == 0.0) {
This basically asks, did the user express no preference for the item? If so,
then the item is recommendable. This ought to work fine for boolean preferences
too.
But then actually, ToItemPrefsMapper assumes you have preference values. That,
I can change. There's no need for that assumption -- it can default to 1.0f.
But then I don't understand your comment about ToUserVectorReducer? I think
it's still important for scalability to perhaps cap the size of vectors.
> org.apache.mahout.cf.taste.hadoop.item.RecommenderJob for Boolean
> recommendation
> --------------------------------------------------------------------------------
>
> 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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