protected void initialize() { final RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][rank]; itemVectors = new double[dataModel.getNumItems()][rank]; final 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 < rank; feature++) { userVectors[userIndex][feature] = random.nextGaussian() * NOISE; } } 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 < rank; feature++) { itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE; } } }
protected void initialize() throws TasteException { RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][rank]; itemVectors = new double[dataModel.getNumItems()][rank]; 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 < rank; feature++) { userVectors[userIndex][feature] = random.nextGaussian() * NOISE; } } 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 < rank; feature++) { itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE; } } }
protected void initialize() throws TasteException { RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][rank]; itemVectors = new double[dataModel.getNumItems()][rank]; 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 < rank; feature++) { userVectors[userIndex][feature] = random.nextGaussian() * NOISE; } } 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 < rank; feature++) { itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE; } } }
protected void initialize() throws TasteException { RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][rank]; itemVectors = new double[dataModel.getNumItems()][rank]; 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 < rank; feature++) { userVectors[userIndex][feature] = random.nextGaussian() * NOISE; } } 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 < rank; feature++) { itemVectors[itemIndex][feature] = random.nextGaussian() * NOISE; } } }
protected void prepareTraining() { final RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][numFeatures]; itemVectors = new double[dataModel.getNumItems()][numFeatures]; final 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; } } cachePreferences(); shufflePreferences(); }
protected void prepareTraining() throws TasteException { RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][numFeatures]; itemVectors = new double[dataModel.getNumItems()][numFeatures]; 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; } } cachePreferences(); shufflePreferences(); }
protected void prepareTraining() throws TasteException { RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][numFeatures]; itemVectors = new double[dataModel.getNumItems()][numFeatures]; 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; } } cachePreferences(); shufflePreferences(); }
protected void prepareTraining() throws TasteException { RandomWrapper random = RandomUtils.getRandom(); userVectors = new double[dataModel.getNumUsers()][numFeatures]; itemVectors = new double[dataModel.getNumItems()][numFeatures]; 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; } } cachePreferences(); shufflePreferences(); }