Example usage for org.apache.mahout.cf.taste.model Preference getValue

List of usage examples for org.apache.mahout.cf.taste.model Preference getValue

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Introduction

In this page you can find the example usage for org.apache.mahout.cf.taste.model Preference getValue.

Prototype

float getValue();

Source Link

Usage

From source file:GavaFactorizer.java

License:Apache License

double getAveragePreference() throws TasteException {
    RunningAverage average = new FullRunningAverage();
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
        for (Preference pref : dataModel.getPreferencesFromUser(it.nextLong())) {
            average.addDatum(pref.getValue());
        }/*from   w  ww.  ja va2s. c  o  m*/
    }
    return average.getAverage();
}

From source file:com.anjuke.romar.mahout.util.Util.java

License:Apache License

public static void applyAdd(FastByIDMap<PreferenceArray> data, Preference addPreference) {
    long userID = addPreference.getUserID();
    long itemID = addPreference.getItemID();
    float preferenceValue = addPreference.getValue();
    PreferenceArray prefs = data.get(userID);
    boolean exists = false;
    if (prefs != null) {
        for (int i = 0; i < prefs.length(); i++) {
            if (prefs.getItemID(i) == itemID) {
                exists = true;//from w w  w  .j a v  a  2s .com
                prefs.setValue(i, preferenceValue);
                break;
            }
        }
    }

    if (!exists) {
        if (prefs == null) {
            prefs = new GenericUserPreferenceArray(1);
        } else {
            PreferenceArray newPrefs = new GenericUserPreferenceArray(prefs.length() + 1);
            for (int i = 0, j = 1; i < prefs.length(); i++, j++) {
                newPrefs.set(j, prefs.get(i));
            }
            prefs = newPrefs;
        }
        prefs.setUserID(0, userID);
        prefs.setItemID(0, itemID);
        prefs.setValue(0, preferenceValue);
        data.put(userID, prefs);
    }

}

From source file:com.innometrics.integration.app.recommender.ml.als.ALSWRFactorizer.java

License:Apache License

protected Vector sparseItemRatingVector(PreferenceArray prefs) {
    SequentialAccessSparseVector ratings = new SequentialAccessSparseVector(Integer.MAX_VALUE, prefs.length());
    for (Preference preference : prefs) {
        ratings.set(userIndex(preference.getUserID()), preference.getValue());
    }//from w  w  w  .j  a  va 2  s.  c o m
    return ratings;
}

From source file:com.innometrics.integration.app.recommender.ml.als.ALSWRFactorizer.java

License:Apache License

protected Vector sparseUserRatingVector(PreferenceArray prefs) {
    SequentialAccessSparseVector ratings = new SequentialAccessSparseVector(Integer.MAX_VALUE, prefs.length());
    for (Preference preference : prefs) {
        ratings.set(itemIndex(preference.getItemID()), preference.getValue());
    }//from  w  w  w  . j a  v  a 2s  .  c  om
    return ratings;
}

From source file:com.msiiplab.recsys.rwr.GLRecommenderIRStatsEvaluator.java

License:Apache License

protected double computeRNDCG(PreferenceArray userPreferences, PreferenceArray userPredictions,
        FastIDSet relevantItemIDs, FastByIDMap<Preference> userPreferenceMap, int at) {
    double rCumulativeGain = 0.0;
    double rIdealizedGain = 0.0;
    // compute IDCG
    Iterator<Preference> it_pref = userPreferences.iterator();
    int i = 0;//w  ww . j av a2s. c  o m
    while (it_pref.hasNext()) {
        Preference pref = it_pref.next();
        if (i < at) {
            double discount = 1.0 / log2(i + 2.0);
            float rel = pref.getValue();
            rIdealizedGain += rel * discount;
        } else {
            break;
        }
        i++;
    }
    // compute DCG
    it_pref = userPredictions.iterator();
    i = 0;
    while (it_pref.hasNext()) {
        Preference pref = it_pref.next();
        long itemID = pref.getItemID();
        if (relevantItemIDs.contains(itemID) && i < at) {
            double discount = 1.0 / log2(i + 2.0);
            float rel = userPreferenceMap.get(itemID).getValue();
            rCumulativeGain += rel * discount;
        }
        i++;
    }
    if (rIdealizedGain > 0) {
        return rCumulativeGain / rIdealizedGain;
    } else {
        return 0;
    }
}

From source file:com.mykidscart.mahout_service.RecommenderServlet.java

License:Apache License

private void writeDebugRecommendations(long userID, Iterable<RecommendedItem> items, PrintWriter writer)
        throws TasteException {
    DataModel dataModel = recommender.getDataModel();
    writer.print("User:");
    writer.println(userID);/*www  . j a va2  s.c  o  m*/
    writer.print("Recommender: ");
    writer.println(recommender);
    writer.println();
    writer.print("Top ");
    writer.print(NUM_TOP_PREFERENCES);
    writer.println(" Preferences:");
    PreferenceArray rawPrefs = dataModel.getPreferencesFromUser(userID);
    int length = rawPrefs.length();
    PreferenceArray sortedPrefs = rawPrefs.clone();
    sortedPrefs.sortByValueReversed();
    // Cap this at NUM_TOP_PREFERENCES just to be brief
    int max = Math.min(NUM_TOP_PREFERENCES, length);
    for (int i = 0; i < max; i++) {
        Preference pref = sortedPrefs.get(i);
        writer.print(pref.getValue());
        writer.print('\t');
        writer.println(pref.getItemID());
    }
    writer.println();
    writer.println("Recommendations:");
    for (RecommendedItem recommendedItem : items) {
        writer.print(recommendedItem.getValue());
        writer.print('\t');
        writer.println(recommendedItem.getItemID());
    }
}

From source file:com.predictionmarketing.itemrecommend.CliMF.java

License:Apache License

public void computeBias() throws TasteException {
    //user bias//from   ww w .j av  a2s . co m
    double userAverage = 0;
    double userMax = -Double.MAX_VALUE;
    double userMin = Double.MAX_VALUE;
    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
        long userid = userIDs.nextLong();
        double user_sum = 0;
        for (Preference record : dataModel.getPreferencesFromUser(userid)) {
            user_sum += record.getValue();
        }
        userAverage += user_sum;
        userMax = (user_sum > userMax) ? user_sum : userMax;
        userMin = (user_sum < userMin) ? user_sum : userMin;
        int userindex = userIndex(userid);
        userVectors[userindex][USER_BIAS_INDEX] = user_sum;
    }
    userAverage /= dataModel.getNumUsers();
    double min_dist = userAverage - userMin;
    double max_dist = userMax - userAverage;
    double normalize = (min_dist > max_dist) ? min_dist : max_dist;
    userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
        long userid = userIDs.nextLong();
        int userindex = userIndex(userid);
        userVectors[userindex][USER_BIAS_INDEX] -= userAverage;
        userVectors[userindex][USER_BIAS_INDEX] /= normalize;
    }

    //item bias
    double itemAverage = 0;
    double itemMax = -Double.MAX_VALUE;
    double itemMin = Double.MAX_VALUE;
    LongPrimitiveIterator itemIDs = dataModel.getItemIDs();
    while (itemIDs.hasNext()) {
        long itemid = itemIDs.nextLong();
        double item_sum = 0;
        for (Preference record : dataModel.getPreferencesForItem(itemid)) {
            item_sum += record.getValue();
        }
        itemAverage += item_sum;
        itemMax = (item_sum > itemMax) ? item_sum : itemMax;
        itemMin = (item_sum < itemMin) ? item_sum : itemMin;
        int itemindex = itemIndex(itemid);
        itemVectors[itemindex][ITEM_BIAS_INDEX] = item_sum;
    }
    itemAverage /= dataModel.getNumItems();
    min_dist = itemAverage - itemMin;
    max_dist = itemMax - itemAverage;
    normalize = (min_dist > max_dist) ? min_dist : max_dist;
    itemIDs = dataModel.getItemIDs();
    while (itemIDs.hasNext()) {
        long itemid = itemIDs.nextLong();
        int itemindex = itemIndex(itemid);
        itemVectors[itemindex][ITEM_BIAS_INDEX] -= itemAverage;
        itemVectors[itemindex][ITEM_BIAS_INDEX] /= normalize;
    }
}

From source file:com.predictionmarketing.itemrecommend.RatingSGDFactorizer.java

License:Apache License

protected void prepareTraining() throws TasteException {
    RandomWrapper random = (RandomWrapper) RandomUtils.getRandom(0L);
    userVectors = new double[dataModel.getNumUsers()][numFeatures];
    itemVectors = new double[dataModel.getNumItems()][numFeatures];
    LongPrimitiveIterator socialuser = SocialdataModel.getUserIDs();
    while (socialuser.hasNext()) { //create social date model idset because user may have no friend ,and it will cause bug
        long userID = socialuser.nextLong();
        SocialIDset.add(userID);/*from   w w  w  .  j a va  2s  .c om*/
    }
    double globalAverage = getAveragePreference();
    for (int userIndex = 0; userIndex < userVectors.length; userIndex++) {
        userVectors[userIndex][0] = globalAverage;
        userVectors[userIndex][USER_BIAS_INDEX] = 0; // will store user bias
        userVectors[userIndex][ITEM_BIAS_INDEX] = 1; // corresponding item feature contains item bias
        for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++) {
            userVectors[userIndex][feature] = random.nextGaussian() * randomNoise;
        }
    }
    for (int itemIndex = 0; itemIndex < itemVectors.length; itemIndex++) {
        itemVectors[itemIndex][0] = 1; // corresponding user feature contains global average
        itemVectors[itemIndex][USER_BIAS_INDEX] = 1; // corresponding user feature contains user bias
        itemVectors[itemIndex][ITEM_BIAS_INDEX] = 0; // will store item bias
        for (int feature = FEATURE_OFFSET; feature < numFeatures; feature++) {
            itemVectors[itemIndex][feature] = random.nextGaussian() * randomNoise;
        }
    }
    //compute bias
    //user bias
    double userAverage = 0;
    double userMax = -Double.MAX_VALUE;
    double userMin = Double.MAX_VALUE;
    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
        long userid = userIDs.nextLong();
        double user_sum = 0;
        for (Preference record : dataModel.getPreferencesFromUser(userid)) {
            user_sum += record.getValue();
        }
        userAverage += user_sum;
        userMax = (user_sum > userMax) ? user_sum : userMax;
        userMin = (user_sum < userMin) ? user_sum : userMin;
        int userindex = userIndex(userid);
        userVectors[userindex][USER_BIAS_INDEX] = user_sum;
    }
    userAverage /= dataModel.getNumUsers();
    double min_dist = userAverage - userMin;
    double max_dist = userMax - userAverage;
    double normalize = (min_dist > max_dist) ? min_dist : max_dist;
    userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
        long userid = userIDs.nextLong();
        int userindex = userIndex(userid);
        userVectors[userindex][USER_BIAS_INDEX] -= userAverage;
        userVectors[userindex][USER_BIAS_INDEX] /= normalize;
    }
    //item bias
    double itemAverage = 0;
    double itemMax = -Double.MAX_VALUE;
    double itemMin = Double.MAX_VALUE;
    LongPrimitiveIterator itemIDs = dataModel.getItemIDs();
    while (itemIDs.hasNext()) {
        long itemid = itemIDs.nextLong();
        double item_sum = 0;
        for (Preference record : dataModel.getPreferencesForItem(itemid)) {
            item_sum += record.getValue();
        }
        itemAverage += item_sum;
        itemMax = (item_sum > itemMax) ? item_sum : itemMax;
        itemMin = (item_sum < itemMin) ? item_sum : itemMin;
        int itemindex = itemIndex(itemid);
        itemVectors[itemindex][ITEM_BIAS_INDEX] = item_sum;
    }
    itemAverage /= dataModel.getNumItems();
    min_dist = itemAverage - itemMin;
    max_dist = itemMax - itemAverage;
    normalize = (min_dist > max_dist) ? min_dist : max_dist;
    itemIDs = dataModel.getItemIDs();
    while (itemIDs.hasNext()) {
        long itemid = itemIDs.nextLong();
        int itemindex = itemIndex(itemid);
        itemVectors[itemindex][ITEM_BIAS_INDEX] -= itemAverage;
        itemVectors[itemindex][ITEM_BIAS_INDEX] /= normalize;
    }

    cachePreferences();
    shufflePreferences();
}

From source file:de.tuberlin.dima.recsys.ssnmm.interactioncut.BiasedItemBasedRecommender.java

License:Apache License

public BiasedItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity, int k, double lambda2,
        double lambda3) throws TasteException {
    super(dataModel, similarity);
    this.k = k;/*from   w  w w .j  a va2  s . co  m*/
    this.similarity = similarity;

    RunningAverage averageRating = new FullRunningAverage();
    LongPrimitiveIterator itemIDs = getDataModel().getItemIDs();
    while (itemIDs.hasNext()) {
        for (Preference pref : getDataModel().getPreferencesForItem(itemIDs.next())) {
            averageRating.addDatum(pref.getValue());
        }
    }

    mu = averageRating.getAverage();

    itemBiases = new OpenLongDoubleHashMap(getDataModel().getNumItems());
    userBiases = new OpenLongDoubleHashMap(getDataModel().getNumUsers());

    itemIDs = getDataModel().getItemIDs();
    while (itemIDs.hasNext()) {
        long itemID = itemIDs.nextLong();
        PreferenceArray preferences = getDataModel().getPreferencesForItem(itemID);
        double sum = 0;
        for (Preference pref : preferences) {
            sum += pref.getValue() - mu;
        }
        double bi = sum / (lambda2 + preferences.length());
        itemBiases.put(itemID, bi);
    }

    LongPrimitiveIterator userIDs = getDataModel().getUserIDs();
    while (userIDs.hasNext()) {
        long userID = userIDs.nextLong();
        PreferenceArray preferences = getDataModel().getPreferencesFromUser(userID);
        double sum = 0;
        for (Preference pref : preferences) {
            sum += pref.getValue() - mu - itemBiases.get(pref.getItemID());
        }
        double bu = sum / (lambda3 + preferences.length());
        userBiases.put(userID, bu);
    }
}

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

License:Apache License

protected Float calculateAverageRating(long userID) throws TasteException {
    Float avgPref = null;/*ww w  . j a v a  2  s  .  c  o m*/
    DataModel dataModel = getDataModel();
    PreferenceArray preferences = dataModel.getPreferencesFromUser(userID);
    float sum = 0;
    for (int i = 0; i < preferences.length(); i++) {
        Preference preference = preferences.get(i);
        sum += preference.getValue();
    }
    if (preferences.length() > 0) {
        avgPref = sum / preferences.length();
    } else {
        avgPref = 0.0f;
    }
    return avgPref;
}