Example usage for org.apache.mahout.cf.taste.impl.recommender ByValueRecommendedItemComparator getInstance

Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.impl.recommender ByValueRecommendedItemComparator getInstance.

Prototype

public static Comparator<RecommendedItem> getInstance() 

Source Link

Usage

From source file:nl.gridline.zieook.inx.movielens.AggregateAndRecommendReducer.java

License:Apache License

/**
 * find the top entries in recommendationVector, map them to the real itemIDs and write back the result
 *//*from  w w w .  j  a v  a 2s  .  c  om*/
private void writeRecommendedItems(VarLongWritable userID, Vector recommendationVector, Context context)
        throws IOException, InterruptedException {
    Queue<RecommendedItem> topItems = new PriorityQueue<RecommendedItem>(recommendationsPerUser + 1,
            Collections.reverseOrder(ByValueRecommendedItemComparator.getInstance()));

    Iterator<Vector.Element> recommendationVectorIterator = recommendationVector.iterateNonZero();
    while (recommendationVectorIterator.hasNext()) {
        Vector.Element element = recommendationVectorIterator.next();
        int index = element.index();

        long itemID = indexItemIDMap.get(index);
        if (itemsToRecommendFor == null || itemsToRecommendFor.contains(itemID)) {
            float value = (float) element.get();
            if (!Float.isNaN(value)) {
                if (topItems.size() < recommendationsPerUser) {
                    topItems.add(new GenericRecommendedItem(itemID, value));
                } else if (value > topItems.peek().getValue()) {
                    topItems.add(new GenericRecommendedItem(itemID, value));
                    topItems.poll();
                }
            }
        }
    }

    if (!topItems.isEmpty()) {
        List<RecommendedItem> recommendations = new ArrayList<RecommendedItem>(topItems.size());
        recommendations.addAll(topItems);
        Collections.sort(recommendations, ByValueRecommendedItemComparator.getInstance());
        context.write(userID, new RecommendedItemsWritable(recommendations));
    }
}

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From source file:org.plista.kornakapi.core.recommender.ArrayTopItems.java

License:Apache License

public static List<RecommendedItem> getTopItems(int howMany, long[] possibleItemIDs, int fromIndex, int toIndex,
        IDRescorer rescorer, TopItems.Estimator<Long> estimator) throws TasteException {

    Preconditions.checkArgument(possibleItemIDs != null, "possibleItemIDs is null");
    Preconditions.checkArgument(estimator != null, "estimator is null");

    Queue<RecommendedItem> topItems = new PriorityQueue<RecommendedItem>(howMany + 1,
            Collections.reverseOrder(ByValueRecommendedItemComparator.getInstance()));
    boolean full = false;
    double lowestTopValue = Double.NEGATIVE_INFINITY;
    for (int index = fromIndex; index < toIndex; index++) {
        long itemID = possibleItemIDs[index];
        if (rescorer == null || !rescorer.isFiltered(itemID)) {
            double preference;
            try {
                preference = estimator.estimate(itemID);
            } catch (NoSuchItemException nsie) {
                continue;
            } catch (NoSuchUserException nsue) {
                continue;
            }/*from www . j  ava 2 s.  co  m*/
            double rescoredPref = rescorer == null ? preference : rescorer.rescore(itemID, preference);
            if (!Double.isNaN(rescoredPref) && (!full || rescoredPref > lowestTopValue)) {
                topItems.add(new GenericRecommendedItem(itemID, (float) rescoredPref));
                if (full) {
                    topItems.poll();
                } else if (topItems.size() > howMany) {
                    full = true;
                    topItems.poll();
                }
                lowestTopValue = topItems.peek().getValue();
            }
        }
    }
    int size = topItems.size();
    if (size == 0) {
        return Collections.emptyList();
    }
    List<RecommendedItem> result = Lists.newArrayListWithCapacity(size);
    result.addAll(topItems);
    Collections.sort(result, ByValueRecommendedItemComparator.getInstance());
    return result;
}

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From source file:org.plista.kornakapi.core.recommender.ParallelTopItems.java

License:Apache License

public static List<RecommendedItem> getTopItems(int howMany, int numThreads, FastIDSet possibleItemIDs,
        final IDRescorer rescorer, final TopItems.Estimator<Long> estimator) throws TasteException {

    Preconditions.checkArgument(numThreads > 1);
    Preconditions.checkNotNull(possibleItemIDs);
    Preconditions.checkNotNull(estimator);

    //long s = System.currentTimeMillis();

    long[] itemIDsToEstimate = possibleItemIDs.toArray();

    //long d = System.currentTimeMillis() - s;
    //System.out.println("Preparation " + d);

    //s = System.currentTimeMillis();
    ExecutorService queue = Executors.newFixedThreadPool(numThreads);

    int fromIndex = 0;
    List<EstimationWorker> estimatorWorkers = Lists.newArrayListWithCapacity(numThreads);
    for (int n = 0; n < numThreads; n++) {

        int toIndex = Math.min(fromIndex + itemIDsToEstimate.length / numThreads, itemIDsToEstimate.length);
        EstimationWorker worker = new EstimationWorker(howMany, itemIDsToEstimate, fromIndex, toIndex, rescorer,
                estimator);/*from   ww  w.j ava  2 s.c  o  m*/
        estimatorWorkers.add(worker);
        queue.execute(worker);
        fromIndex = toIndex;
    }

    queue.shutdown();
    try {
        queue.awaitTermination(5, TimeUnit.SECONDS);
    } catch (InterruptedException e) {
        throw new RuntimeException(e);
    }
    //d = System.currentTimeMillis() - s;
    //System.out.println("Estimation " + d);

    List<RecommendedItem> topItems = Lists.newArrayList();
    for (int n = 0; n < numThreads; n++) {
        topItems.addAll(estimatorWorkers.get(n).topItems);
    }

    if (topItems.isEmpty()) {
        return Collections.EMPTY_LIST;
    }
    if (topItems.size() < howMany) {
        howMany = topItems.size();
    }
    Collections.sort(topItems, ByValueRecommendedItemComparator.getInstance());
    List<RecommendedItem> recommendedItems = topItems.subList(0, howMany);

    return recommendedItems;
}

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