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https://issues.apache.org/jira/browse/MAHOUT-359?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12852850#action_12852850
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Hui Wen Han commented on MAHOUT-359:
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if it can optimize to avoid the multiplication and avoid to
findTopNPrefsCutoff() for Non-existence preferences,
it will improve the performance.
also find issue is :
UserVectorToCooccurrenceMapper can has 2 map task maximumly (I need test more
here )
> 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
> @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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