List of usage examples for org.apache.mahout.cf.taste.impl.recommender GenericBooleanPrefUserBasedRecommender GenericBooleanPrefUserBasedRecommender
public GenericBooleanPrefUserBasedRecommender(DataModel dataModel, UserNeighborhood neighborhood,
UserSimilarity similarity)
From source file:com.buddycloud.channeldirectory.search.handler.common.mahout.ChannelRecommender.java
License:Apache License
public ChannelRecommender(Properties properties) throws TasteException { this.recommenderDataModel = createDataModel(properties); DataModel dataModel = recommenderDataModel.getDataModel(); UserSimilarity userSimilarity = new CachingUserSimilarity(new LogLikelihoodSimilarity(dataModel), MAX_CACHE_SIZE);// w w w .j a va 2 s. c o m this.userNeighborhood = new NearestNUserNeighborhood(10, Double.NEGATIVE_INFINITY, userSimilarity, dataModel, 1.0); this.userRecommender = new GenericBooleanPrefUserBasedRecommender(dataModel, userNeighborhood, userSimilarity); this.itemSimilarity = new LogLikelihoodSimilarity(dataModel); }
From source file:com.mycompany.xplor_recommendation_engine.Xplor.java
/** * @param args the command line arguments * @throws java.sql.SQLException/* w w w . j a v a 2 s . c o m*/ * @throws java.io.IOException */ public static void main(String[] args) throws SQLException, IOException, TasteException { FileConverter fc = FileConverter.getFileConverter(); int[] columnSpecs = new int[2]; columnSpecs[0] = 2; columnSpecs[1] = 2; fc.convertToCSV("xplor_development", "blog_profile_maps", columnSpecs); // Calibrate recommender for csv file DataModel model = new FileDataModel(new File("blog_profile_maps.csv")); UserSimilarity similarity = new TanimotoCoefficientSimilarity(model); UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model); Recommender recommender = new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity); Recommender cachingRecommender = new CachingRecommender(recommender); // Establish JDBC connection for (int i = 1; i <= model.getNumUsers(); i++) { List<RecommendedItem> recommendations = cachingRecommender.recommend(i, 10); for (RecommendedItem recommendation : recommendations) { // Store recommendations in recommendations table } } // Store recommendations in database }
From source file:edu.nudt.c6.datasetlinking.mahout.MyRecommenderBuilder.java
License:Apache License
@Override public Recommender buildRecommender(DataModel dataModel) throws TasteException { if (recommenderType == RECOMMENDER.ITEM) { ItemSimilarity itemSimilarity = null; switch (similarityType) { case PEARSON: itemSimilarity = new PearsonCorrelationSimilarity(dataModel); break; case PEARSON_WEIGHTED: itemSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED); break; case COSINE: itemSimilarity = new UncenteredCosineSimilarity(dataModel); break; case TANIMOTO: itemSimilarity = new TanimotoCoefficientSimilarity(dataModel); break; case LOGLIKELIHOOD: itemSimilarity = new LogLikelihoodSimilarity(dataModel); break; case CITYBLOCK: itemSimilarity = new CityBlockSimilarity(dataModel); break; case EUCLIDEAN: itemSimilarity = new EuclideanDistanceSimilarity(dataModel); break; case EUCLIDEAN_WEIGHTED: itemSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED); break; case DATASET_VOCABULARY_COSINE: try { itemSimilarity = new DatasetVocabularySimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace();// www.ja va 2s . c o m } break; case DATASET_CLASS_COSINE: try { itemSimilarity = new DatasetClassSimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; case DATASET_PPROPERTY_COSINE_SUBJECTS: try { itemSimilarity = new DatasetPropertySubjectsSimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; case DATASET_PPROPERTY_COSINE_TRIPLES: try { itemSimilarity = new DatasetPropertyTriplesSimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; default: itemSimilarity = new EuclideanDistanceSimilarity(dataModel); } if (pref) { return new GenericItemBasedRecommender(dataModel, itemSimilarity); } else { return new GenericBooleanPrefItemBasedRecommender(dataModel, itemSimilarity); } } else if (recommenderType == RECOMMENDER.USER) { UserSimilarity userSimilarity = null; switch (similarityType) { case PEARSON: userSimilarity = new PearsonCorrelationSimilarity(dataModel); break; case PEARSON_WEIGHTED: userSimilarity = new PearsonCorrelationSimilarity(dataModel, Weighting.WEIGHTED); break; case COSINE: userSimilarity = new UncenteredCosineSimilarity(dataModel); break; case SPEARMAN: userSimilarity = new SpearmanCorrelationSimilarity(dataModel); break; case TANIMOTO: userSimilarity = new TanimotoCoefficientSimilarity(dataModel); break; case LOGLIKELIHOOD: userSimilarity = new LogLikelihoodSimilarity(dataModel); break; case CITYBLOCK: userSimilarity = new CityBlockSimilarity(dataModel); break; case EUCLIDEAN: userSimilarity = new EuclideanDistanceSimilarity(dataModel); break; case EUCLIDEAN_WEIGHTED: userSimilarity = new EuclideanDistanceSimilarity(dataModel, Weighting.WEIGHTED); break; case DATASET_VOCABULARY_COSINE: try { userSimilarity = new DatasetVocabularySimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; case DATASET_CLASS_COSINE: try { userSimilarity = new DatasetClassSimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; case DATASET_PPROPERTY_COSINE_SUBJECTS: try { userSimilarity = new DatasetPropertySubjectsSimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; case DATASET_PPROPERTY_COSINE_TRIPLES: try { userSimilarity = new DatasetPropertyTriplesSimilarity(dataModel); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; default: userSimilarity = new EuclideanDistanceSimilarity(dataModel); } UserNeighborhood userNeighborhood = null; switch (neighborhoodType) { case NEAREST: userNeighborhood = new NearestNUserNeighborhood(this.nearestNum, userSimilarity, dataModel); break; case THRESHOLD: default: userNeighborhood = new ThresholdUserNeighborhood(this.neighborThreshold, userSimilarity, dataModel); } if (pref) { return new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity); } else { return new GenericBooleanPrefUserBasedRecommender(dataModel, userNeighborhood, userSimilarity); } } else if (recommenderType == RECOMMENDER.SVD) { AbstractFactorizer factorizer = null; switch (SVDfactorizerType) { case RatingSGD: factorizer = new RatingSGDFactorizer(dataModel, factorNum, iterationNum); break; case ALSWR: factorizer = new ALSWRFactorizer(dataModel, factorNum, lambda, iterationNum); break; case SVDPlusPlus: factorizer = new SVDPlusPlusFactorizer(dataModel, factorNum, iterationNum); break; case ParallelSGD: factorizer = new ParallelSGDFactorizer(dataModel, factorNum, lambda, iterationNum); break; case MyRatingSGD: factorizer = new MyRatingSGDFactorizer(dataModel, factorNum, iterationNum); break; } return new SVDRecommender(dataModel, factorizer); } else if (recommenderType == RECOMMENDER.LINKDOCUMENT) { } else if (recommenderType == RECOMMENDER.CollaborativeRanking) { AbstractCRFactorizer factorizer = null; switch (CRFactorizerType) { case BasicLFM: try { factorizer = new BasicLFMFactorizer(dataModel, factorNum, iterationNum); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; case LFMTrans: try { factorizer = new LFMTransFactorizer(dataModel, factorNum, iterationNum, learningRate); } catch (ClassNotFoundException | IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } break; } return new CollaborativeRankingRecommender(dataModel, factorizer); } else if (recommenderType == RECOMMENDER.Random) { return new RandomRecommender(dataModel); } else if (recommenderType == RECOMMENDER.ItemAverage) { return new ItemAverageRecommender(dataModel); } else if (recommenderType == RECOMMENDER.ItemUserAverage) { return new ItemUserAverageRecommender(dataModel); } return null; }
From source file:org.easyrec.plugin.mahout.MahoutBooleanGenerator.java
License:Open Source License
@Override protected void doExecute(ExecutionControl executionControl, MahoutBooleanGeneratorStats stats) throws Exception { // when doExecute() is called, the generator has been initialized with the configuration we should use Date execution = new Date(); MahoutBooleanGeneratorConfig config = getConfiguration(); TypeMappingService typeMappingService = (TypeMappingService) super.getTypeMappingService(); ItemAssocService itemAssocService = getItemAssocService(); executionControl.updateProgress("initialize DataModel"); DataModel easyrecDataModel = new EasyrecDataModel(config.getTenantId(), typeMappingService.getIdOfActionType(config.getTenantId(), config.getActionType()), false, mahoutDataModelMappingDAO);// ww w . jav a 2 s . com if (config.getCacheDataInMemory() == 1) { executionControl.updateProgress("initialize EasyrecInMemoryDataModel"); easyrecDataModel = new EasyrecInMemoryDataModel(easyrecDataModel); } /*TanimotoCoefficientSimilarity is intended for "binary" data sets where a user either expresses a generic "yes" preference for an item or has no preference.*/ UserSimilarity userSimilarity = null; switch (config.getUserSimilarityMethod()) { case 1: executionControl.updateProgress("using LogLikelihoodSimilarity as UserSimilarity"); userSimilarity = new LogLikelihoodSimilarity(easyrecDataModel); break; case 2: executionControl.updateProgress("using TanimotoCoefficientSimilarity as UserSimilarity"); userSimilarity = new TanimotoCoefficientSimilarity(easyrecDataModel); break; case 3: executionControl.updateProgress("using SpearmanCorrelationSimilarity as UserSimilarity"); userSimilarity = new SpearmanCorrelationSimilarity(easyrecDataModel); break; case 4: executionControl.updateProgress("using CityBlockSimilarity as UserSimilarity"); userSimilarity = new CityBlockSimilarity(easyrecDataModel); break; } /*ThresholdUserNeighborhood is preferred in situations where we go in for a similarity measure between neighbors and not any number*/ UserNeighborhood neighborhood = null; Double userNeighborhoodSamplingRate = config.getUserNeighborhoodSamplingRate(); Double neighborhoodThreshold = config.getUserNeighborhoodThreshold(); int neighborhoodSize = config.getUserNeighborhoodSize(); double userNeighborhoodMinSimilarity = config.getUserNeighborhoodMinSimilarity(); switch (config.getUserNeighborhoodMethod()) { case 1: executionControl.updateProgress("using ThresholdUserNeighborhood as UserNeighborhood"); neighborhood = new ThresholdUserNeighborhood(neighborhoodThreshold, userSimilarity, easyrecDataModel, userNeighborhoodSamplingRate); break; case 2: executionControl.updateProgress("using NearestNUserNeighborhood as UserNeighborhood"); neighborhood = new NearestNUserNeighborhood(neighborhoodSize, userNeighborhoodMinSimilarity, userSimilarity, easyrecDataModel, userNeighborhoodSamplingRate); break; } /*GenericBooleanPrefUserBasedRecommender is appropriate for use when no notion of preference value exists in the data. */ executionControl.updateProgress("using GenericBooleanPrefUserBasedRecommender as Recommender"); Recommender recommender = new GenericBooleanPrefUserBasedRecommender(easyrecDataModel, neighborhood, userSimilarity); itemTypeDAO.insertOrUpdate(config.getTenantId(), "USER", true); Integer assocType = typeMappingService.getIdOfAssocType(config.getTenantId(), config.getAssociationType()); Integer userType = typeMappingService.getIdOfItemType(config.getTenantId(), "USER"); Integer sourceType = typeMappingService.getIdOfSourceType(config.getTenantId(), getId().toString()); Integer viewType = typeMappingService.getIdOfViewType(config.getTenantId(), config.getViewType()); stats.setNumberOfItems(easyrecDataModel.getNumItems()); int totalSteps = easyrecDataModel.getNumUsers(); int currentStep = 1; for (LongPrimitiveIterator it = easyrecDataModel.getUserIDs(); it.hasNext() && !executionControl.isAbortRequested();) { executionControl.updateProgress(currentStep++, totalSteps, "Saving Recommendations..."); long userId = it.nextLong(); List<RecommendedItem> recommendations = recommender.recommend(userId, config.getNumberOfRecs()); if (recommendations.isEmpty()) { logger.debug("User " + userId + " : no recommendations"); } // print the list of recommendations for each for (RecommendedItem recommendedItem : recommendations) { logger.debug("User " + userId + " : " + recommendedItem); Integer itemToId = (int) recommendedItem.getItemID(); Integer itemToType = itemDAO.getItemTypeIdOfItem(config.getTenantId(), itemToId); ItemVO<Integer, Integer> fromItem = new ItemVO<Integer, Integer>(config.getTenantId(), (int) userId, userType); Double recommendationStrength = (double) recommendedItem.getValue(); ItemVO<Integer, Integer> toItem = new ItemVO<Integer, Integer>(config.getTenantId(), itemToId, itemToType); ItemAssocVO<Integer, Integer> itemAssoc = new ItemAssocVO<Integer, Integer>(config.getTenantId(), fromItem, assocType, recommendationStrength, toItem, sourceType, "Mahout Boolean Generator", viewType, null, execution); itemAssocService.insertOrUpdateItemAssoc(itemAssoc); stats.incNumberOfRulesCreated(); } } }
From source file:org.easyrec.plugin.mahout.MahoutBooleanGeneratorTest.java
License:Open Source License
@Test public void mahoutSlopeoneGeneratorTest_testBoolRecommender() throws TasteException { EasyrecDataModel easyrecDataModel = new EasyrecDataModel(TENANT_ID, BUY_ACTION_TYPE_ID, false, mahoutDataModelMappingDAO);//from ww w .ja v a 2 s . c o m /*TanimotoCoefficientSimilarity is intended for "binary" data sets where a user either expresses a generic "yes" preference for an item or has no preference.*/ UserSimilarity userSimilarity = new TanimotoCoefficientSimilarity(easyrecDataModel); /*ThresholdUserNeighborhood is preferred in situations where we go in for a similarity measure between neighbors and not any number*/ UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1d, userSimilarity, easyrecDataModel); /*GenericBooleanPrefUserBasedRecommender is appropriate for use when no notion of preference value exists in the data. */ Recommender recommender = new GenericBooleanPrefUserBasedRecommender(easyrecDataModel, neighborhood, userSimilarity); Assert.assertEquals(30, recommender.recommend(3, 1).get(0).getItemID()); Assert.assertEquals(1, (int) recommender.recommend(3, 1).get(0).getValue()); }
From source file:org.zaizi.mahout.config.UserBasedConfiguration.java
License:Open Source License
public Recommender configure(DataModel dataModel) throws TasteException { UserSimilarity userSimilarity = userSimilarityConfiguration.getUserSimilarity(dataModel); UserNeighborhood neighborhood = neighborhoodConfiguration.getNeighborhood(dataModel, userSimilarity); switch (ratingScheme) { case BOOLEAN_PREF: return new GenericBooleanPrefUserBasedRecommender(dataModel, neighborhood, userSimilarity); case SCORE_PREF: return new GenericUserBasedRecommender(dataModel, neighborhood, userSimilarity); default:// w w w. j a va 2 s .c om throw new TasteException("Only Boolean or Score rating supported."); } }
From source file:tv.icntv.recommend.algorithm.test.RecommendFactory.java
License:Apache License
public static RecommenderBuilder userRecommender(final UserSimilarity us, final UserNeighborhood un, boolean pref) throws TasteException { return pref ? new RecommenderBuilder() { @Override//from www.ja v a 2 s.c o m public Recommender buildRecommender(DataModel model) throws TasteException { return new GenericUserBasedRecommender(model, un, us); } } : new RecommenderBuilder() { @Override public Recommender buildRecommender(DataModel model) throws TasteException { return new GenericBooleanPrefUserBasedRecommender(model, un, us); } }; }
From source file:user.based.recommendation.PearsonCorrelation.java
private void cmboboxUserItemStateChanged(java.awt.event.ItemEvent evt) {//GEN-FIRST:event_cmboboxUserItemStateChanged // TODO add your handling code here: try {/*from w w w.ja v a2 s . c om*/ DataModel model = new FileDataModel(new File("userPreference.csv")); UserSimilarity similarity = new PearsonCorrelationSimilarity(model); //UserSimilarity similarity1 = new SpearmanCorrelationSimilarity(model); UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model); //UserNeighborhood neighborhood1 = new ThresholdUserNeighborhood(0.1, similarity1, model); UserBasedRecommender recommender = new GenericBooleanPrefUserBasedRecommender(model, neighborhood, similarity); UserBasedRecommender recommender1 = new GenericUserBasedRecommender(model, neighborhood, similarity); List<RecommendedItem> recommendations = recommender .recommend(Integer.parseInt(cmboboxUser.getSelectedItem().toString()), 3); List<RecommendedItem> recommendations1 = recommender1 .recommend(Integer.parseInt(cmboboxUser.getSelectedItem().toString()), 3); System.out.println("Recommendation length: " + recommendations.size()); result = new ArrayList<>(); for (RecommendedItem item : recommendations) { System.out.println( table.get(Integer.parseInt(Long.toString(item.getItemID()))) + " : " + item.getValue()); result.add(table.get(Integer.parseInt(Long.toString(item.getItemID()))) + " : " + item.getValue()); } System.out.println("Boolean recommendation: "); for (RecommendedItem item : recommendations1) { // System.out.println(table.get(Integer.parseInt(Long.toString(item.getItemID()))) + " : " + item.getValue()); // result.add(table.get(Integer.parseInt(Long.toString(item.getItemID()))) + " : " + item.getValue()); } lstRecommendedItems.setListData(result.toArray()); } catch (IOException | TasteException | NumberFormatException ex) { System.out.println("Here is the exception: " + ex.getMessage()); } }