Example usage for org.apache.mahout.cf.taste.impl.recommender TopItems getTopItems

Introduction

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

Prototype

public static List<RecommendedItem> getTopItems(int howMany, LongPrimitiveIterator possibleItemIDs,
            IDRescorer rescorer, Estimator<Long> estimator) throws TasteException 

Source Link

Usage

From source file:com.buddycloud.channeldirectory.search.handler.common.mahout.ChannelRecommender.java

License:Apache License

/**
 * Recommends a list of jids of channels that are
 * similar to a given channel.// w ww . j a  va 2  s  . co m
 * 
 * @param channelJid The channel jid
 * @param howMany The number of recommendations
 * @return A list of similar channels' jids 
 * @throws TasteException
 * @throws SQLException 
 */
public RecommendationResponse getSimilarChannels(String channelJid, int howMany)
        throws TasteException, SQLException {

    Long itemId = recommenderDataModel.toChannelId(channelJid);

    if (itemId == null) {
        return new RecommendationResponse(new LinkedList<ChannelData>(), 0);
    }

    TopItems.Estimator<Long> estimator = new MostSimilarEstimator(itemId, itemSimilarity, null);
    MostSimilarItemsCandidateItemsStrategy candidateStrategy = new PreferredItemsNeighborhoodCandidateItemsStrategy();

    FastIDSet possibleItemIDs = candidateStrategy.getCandidateItems(new long[] { itemId },
            recommenderDataModel.getDataModel());
    List<RecommendedItem> recommended = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), null,
            estimator);

    List<ChannelData> recommendedChannels = new LinkedList<ChannelData>();

    for (RecommendedItem recommendedItem : recommended) {
        recommendedChannels.add(recommenderDataModel.toChannelData(recommendedItem.getItemID()));
    }

    return new RecommendationResponse(recommendedChannels, possibleItemIDs.size());
}

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From source file:com.recsys.svd.CustomSVDRecommender.java

License:Apache License

@Override
public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
    Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
    log.debug("Recommending items for user ID '{}'", userID);

    PreferenceArray preferencesFromUser = getDataModel().getPreferencesFromUser(userID);
    FastIDSet possibleItemIDs = getAllOtherItems(userID, preferencesFromUser);

    List<RecommendedItem> topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer,
            new Estimator(userID));
    log.debug("Recommendations are: {}", topItems);

    return topItems;
}

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From source file:com.webir.popcornsaver.cluster.TreeClusteringRecommender.java

License:Apache License

private List<RecommendedItem> computeTopRecsForCluster(FastIDSet cluster) throws TasteException {
    DataModel dataModel = getDataModel();
    FastIDSet possibleItemIDs = new FastIDSet();
    LongPrimitiveIterator it = cluster.iterator();
    while (it.hasNext()) {
        possibleItemIDs.addAll(dataModel.getItemIDsFromUser(it.next()));
    }//from  w  w  w. ja v a 2s .  c  o m

    TopItems.Estimator<Long> estimator = new Estimator(cluster);

    List<RecommendedItem> topItems = TopItems.getTopItems(possibleItemIDs.size(), possibleItemIDs.iterator(),
            null, estimator);

    log.debug("Recommendations are: {}", topItems);
    return Collections.unmodifiableList(topItems);
}

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From source file:de.unima.dws.webmining.rs.recommender.AvgUserPrefAdaptedUserBasedRecommender.java

License:Apache License

public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
    Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");

    log.debug("Recommending items for user ID '{}'", userID);

    long[] theNeighborhood = neighborhood.getUserNeighborhood(userID);

    if (theNeighborhood.length == 0) {
        return Collections.emptyList();
    }/*w w  w.j  a v  a2 s  .  com*/

    FastIDSet allItemIDs = getAllOtherItems(theNeighborhood, userID);
    TopItems.Estimator<Long> estimator = new Estimator(userID, theNeighborhood);
    List<RecommendedItem> topItems = TopItems.getTopItems(howMany, allItemIDs.iterator(), rescorer, estimator);

    log.debug("Recommendations are: {}", topItems);
    return topItems;
}

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From source file:net.ufida.info.mahout.common.SlopeOneRecommender.java

License:Apache License

@Override
public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer) throws TasteException {
    Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
    log.debug("Recommending items for user ID '{}'", userID);

    FastIDSet possibleItemIDs = diffStorage.getRecommendableItemIDs(userID);

    TopItems.Estimator<Long> estimator = new Estimator(userID);

    List<RecommendedItem> topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer,
            estimator); //

    log.debug("Recommendations are: {}", topItems);
    return topItems;
}

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

License:Apache License

@Override
public List<RecommendedItem> recommend(long userID, long[] itemIDs, int howMany, IDRescorer rescorer)
        throws TasteException {
    Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
    log.debug("Recommending items for user ID '{}'", userID);

    PreferenceArray preferencesFromUser = asPreferences(itemIDs);

    long fetchItemIDsStart = System.currentTimeMillis();
    FastIDSet possibleItemIDs = getAllOtherItems(userID, preferencesFromUser);
    long fetchItemIDsDuration = System.currentTimeMillis() - fetchItemIDsStart;

    long estimateStart = System.currentTimeMillis();
    List<RecommendedItem> topItems;
    if (numEstimationThreads > 1) {
        topItems = ParallelTopItems.getTopItems(howMany, numEstimationThreads, possibleItemIDs, rescorer,
                new Estimator(userID));
    } else {/*from   w w w  . j a  v a 2s .com*/
        topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer, new Estimator(userID));
    }
    long estimateDuration = System.currentTimeMillis() - estimateStart;

    long numCandidates = -1;
    if (rescorer != null && rescorer instanceof FixedCandidatesIDRescorer) {
        numCandidates = ((FixedCandidatesIDRescorer) rescorer).numCandidates();
    }

    if (log.isDebugEnabled()) {
        log.debug("fetched {} interactions of user {} ({} itemIDs in {} ms, estimation of {} in {} ms)",
                new Object[] { preferencesFromUser.length(), userID, possibleItemIDs.size(),
                        fetchItemIDsDuration, numCandidates, estimateDuration });

    }

    log.debug("Recommendations are: {}", topItems);

    return topItems;
}

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

License:Apache License

@Override
public List<RecommendedItem> recommendToAnonymous(long[] itemIDs, int howMany, IDRescorer rescorer)
        throws TasteException {

    //TODO what to do here in the non-implicit case? choose a rating?
    long fetchItemIDsStart = System.currentTimeMillis();
    PreferenceArray preferences = asPreferences(itemIDs);
    long fetchItemIDsDuration = System.currentTimeMillis() - fetchItemIDsStart;

    long estimateStart = System.currentTimeMillis();
    double[] foldedInUserFeatures = foldingFactorization.foldInAnonymousUser(itemIDs);

    FastIDSet possibleItemIDs = getAllOtherItems(Long.MIN_VALUE, preferences);
    List<RecommendedItem> topItems;

    if (numEstimationThreads > 1) {
        topItems = ParallelTopItems.getTopItems(howMany, numEstimationThreads, possibleItemIDs, rescorer,
                new AnonymousEstimator(foldedInUserFeatures));
    } else {// ww w  . j  av  a  2 s. c  o m
        topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer,
                new AnonymousEstimator(foldedInUserFeatures));
    }
    log.debug("Recommendations are: {}", topItems);
    long estimateDuration = System.currentTimeMillis() - estimateStart;
    if (log.isDebugEnabled()) {
        log.debug("fetched {} interactions of Anonymous ({} itemIDs in {} ms, estimation in {} ms)",
                new Object[] { preferences.length(), preferences.length(), fetchItemIDsDuration,
                        estimateDuration });

    }
    return topItems;
}

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From source file:recommender.CustomRecommender.java

@Override
public List<RecommendedItem> recommend(long userID, int howMany, IDRescorer rescorer, boolean includeKnownItems)
        throws TasteException {
    Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
    log.debug("Recommending items for user ID '{}'", userID);

    PreferenceArray preferencesFromUser = getDataModel().getPreferencesFromUser(userID);
    FastIDSet possibleItemIDs = getAllOtherItems(userID, preferencesFromUser, includeKnownItems);

    List<RecommendedItem> topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer,
            new Estimator(userID));
    log.debug("Recommendations are: {}", topItems);

    return topItems;
}

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From source file:recommender.MyRecommender.java

public List<RecommendedItem> recommend1(long userID, int howMany, IDRescorer rescorer,
        boolean includeKnownItems) throws TasteException {
    Preconditions.checkArgument(howMany >= 1, "howMany must be at least 1");
    log.debug("Recommending items for user ID '{}'", userID);
    System.out.println("Recommending items for user ID: " + userID);

    PreferenceArray preferencesFromUser = getDataModel().getPreferencesFromUser(userID);
    FastIDSet possibleItemIDs = getAllOtherItems(userID, preferencesFromUser, includeKnownItems);

    List<RecommendedItem> topItems = TopItems.getTopItems(howMany, possibleItemIDs.iterator(), rescorer,
            new Estimator(userID));
    log.debug("Recommendations are: {}", topItems);

    System.out.println("Recommendations are: " + topItems);

    return topItems;
}

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