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

Introduction

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

Prototype

long getItemID();

Source Link

Usage

From source file:GavaFactorizer.java

License:Apache License

private void cachePreferences() throws TasteException {
    int numPreferences = countPreferences();
    cachedUserIDs = new long[numPreferences];
    cachedItemIDs = new long[numPreferences];

    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    int index = 0;
    while (userIDs.hasNext()) {
        long userID = userIDs.nextLong();
        PreferenceArray preferencesFromUser = dataModel.getPreferencesFromUser(userID);
        for (Preference preference : preferencesFromUser) {
            cachedUserIDs[index] = userID;
            cachedItemIDs[index] = preference.getItemID();
            index++;/*from w  w w .j av  a2s  .c  o m*/
        }
    }
}

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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;/*  ww  w  .  ja  v a 2 s . c  o m*/
                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);
    }

}

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

License:Apache License

public static void applyRemove(FastByIDMap<PreferenceArray> data, Preference removePreference) {
    long userID = removePreference.getUserID();
    long itemID = removePreference.getItemID();
    PreferenceArray prefs = data.get(userID);
    if (prefs != null) {
        boolean exists = false;
        int length = prefs.length();
        for (int i = 0; i < length; i++) {
            if (prefs.getItemID(i) == itemID) {
                exists = true;//from  w w w .ja v a2 s.  c o  m
                break;
            }
        }
        if (exists) {
            if (length == 1) {
                data.remove(userID);
            } else {
                PreferenceArray newPrefs = new GenericUserPreferenceArray(length - 1);
                for (int i = 0, j = 0; i < length; i++, j++) {
                    if (prefs.getItemID(i) == itemID) {
                        j--;
                    } else {
                        newPrefs.set(j, prefs.get(i));
                    }
                }
                data.put(userID, newPrefs);
            }
        }
    }

}

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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 www . ja  v  a  2  s  . co m*/
    return ratings;
}

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

License:Apache License

protected FastByIDMap<Preference> getPrefereceMap(PreferenceArray userPreferences) {
    FastByIDMap<Preference> map = new FastByIDMap<Preference>();
    for (Preference pref : userPreferences) {
        map.put(pref.getItemID(), pref);
    }/*w  w  w.j  a v a 2s .  c  om*/
    return map;
}

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

License:Apache License

protected double computeNDCG(PreferenceArray userPreferences, PreferenceArray userPredictions,
        FastIDSet relevantItemIDs, FastByIDMap<Preference> userPreferenceMap, int at) {
    double cumulativeGain = 0.0;
    double idealizedGain = 0.0;
    // compute IDCG
    Iterator<Preference> it_pref = userPreferences.iterator();
    int i = 0;//from w w  w .  j ava  2s . c o  m
    while (it_pref.hasNext()) {
        it_pref.next();
        if (i < at) {
            idealizedGain += 1.0 / log2(i + 2.0);
            ;
        } 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) {
            cumulativeGain += 1.0 / log2(i + 2.0);
            ;
        }
        i++;
    }
    if (idealizedGain > 0) {
        return cumulativeGain / idealizedGain;
    } else {
        return 0;
    }
}

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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;/*ww  w.ja  v  a  2 s .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;
    }
}

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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);//from  w ww .  j  a v  a2  s .c  om
    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());
    }
}

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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 v a  2s . c om
    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);
    }
}

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From source file:edu.uniandes.yelp.recommender.CFRecommender.java

public List<Business> recommend(long userID) {
    try {/*  w  ww .  j ava2  s.co  m*/
        List<RecommendedItem> items = recommender.recommend(userID, 10);
        List<ObjectId> ids = new LinkedList<>();
        System.out.println("user: " + dm.fromLongToId(userID));
        System.out.println("count: " + items.size());
        System.out.println("max preference: " + dm.getMaxPreference());
        System.out.println("min preference: " + dm.getMinPreference());
        for (Preference p : dm.getPreferencesFromUser(userID)) {
            System.out.println("preferencia: item: " + p.getItemID() + " value: " + p.getValue());
        }
        items.stream().forEach((item) -> {
            System.out.println("item : " + item.getItemID());
            ids.add(new ObjectId(dm.fromLongToId(item.getItemID())));
        });
        List<Business> datos = businessService.getBusiness(ids);

        return datos;
    } catch (TasteException ex) {
        Logger.getLogger(CFRecommender.class.getName()).log(Level.SEVERE, null, ex);
        return new LinkedList<>();
    }
}

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