Java RecommendedItem类代码示例

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
public static void main(String[] args) throws IOException, TasteException {
	DataModel model  = 
			new FileDataModel(new File("data/ua.base"));
	
	UserSimilarity similarity = 
			new PearsonCorrelationSimilarity(model);
	
	UserNeighborhood neighborhood = 
			new NearestNUserNeighborhood(2, similarity, model);
	
	Recommender recommender = new GenericUserBasedRecommender(
			model, neighborhood, similarity);
	
	List<RecommendedItem> recommendations = recommender.recommend(2, 1);
	
	for (RecommendedItem recommendation : recommendations) {
		logger.info(recommendation.toString());
	}
	
	logger.info("over");
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
private static void recommend(String ratingsFile, int ... userIds)
    throws TasteException, IOException {
  DataModel model = new FileDataModel(new File(ratingsFile));

  UserSimilarity similarity = new PearsonCorrelationSimilarity(model);

  UserNeighborhood neighborhood =
      new NearestNUserNeighborhood(
          100, similarity, model);

  Recommender recommender =  new GenericUserBasedRecommender(
      model, neighborhood, similarity);

  Recommender cachingRecommender = new CachingRecommender(recommender);

  for(int userId: userIds) {
    System.out.println("UserID " + userId);
    List<RecommendedItem> recommendations =
        cachingRecommender.recommend(userId, 2);
    for(RecommendedItem item: recommendations) {
      System.out.println("  item " + item.getItemID() + " score " + item.getValue());
    }
  }
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
public static void main(String[] args) throws TasteException, IOException {
	DataModel model = new FileDataModel(new File("data/dataset.csv"));
   	ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
   	//UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
   	GenericItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity);
   	List<RecommendedItem> recommendations = recommender.mostSimilarItems(18, 3);
   	for (RecommendedItem recommendation : recommendations) {
   	  System.out.println(recommendation);
   	}

}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
public List<RecommendedItem> recommend(long userId, PreferenceArray preferences) throws TasteException {

		if (userExistsInDataModel(userId)) {
			return recommender.recommend(userId, noItems);
		}
		else {
			PlusAnonymousConcurrentUserDataModel plusModel = (PlusAnonymousConcurrentUserDataModel) recommender.getDataModel();

			// Take an available anonymous user form the poll
			Long anonymousUserID = plusModel.takeAvailableUser();

			// Set temporary preferences
			PreferenceArray tempPrefs = preferences;
			tempPrefs.setUserID(0, anonymousUserID);
			// tempPrefs.setItemID(0, itemID);
			plusModel.setTempPrefs(tempPrefs, anonymousUserID);

			List<RecommendedItem> results = recommender.recommend(anonymousUserID, noItems);

			// Release the user back to the poll
			plusModel.releaseUser(anonymousUserID);

			return results;

		}
	}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
/**
 * Give the recommendations based on the supplied User instance.
 * This will make the calls to Mahout and ask it to give recommendations
 * for the given User instance that this user is likely interested in.
 * Although we speak about Users here, this can be any object type.
 * Note: The recommendations that are returned are the result of the
 * algorithm you use, if the results do not match your expectations, please
 * try using another algorithm. See the documentation for more information.
 * @param x The user object to give recommendations of.
 * @param howMany The number of recommendations you would like to get.
 * @param aType The type of the recommendation, either USER (in case of User
 * to Item recommendations) or ITEM (in case of Item to Item recommendations)
 * @return A list with recommended items that are recommended by Mahout
 * for this user.
 */
public List<?> getRecommendations(Object x, int howMany, RecFieldType aType) {
    if(checkAvailabilityOfRecommendations(aType) == false){
        org.webdsl.logging.Logger.warn("Warning, you request to get recommendations while the recommendations taste table has not yet been filled by Mahout.\nEither wait for it to finish its background task, or call the reconstructRecommendCache function (last option should be used with care, on non-production systems only.)");
        return new ArrayList<Object>();
    }
    long startTime = System.currentTimeMillis();

    try {
        long id = getIDOfObject(x, aType);

        List<RecommendedItem> recommendations = aType == RecFieldType.USER ? this.userRecommenderCache.recommend(id, howMany) : this.itemRecommenderCache.recommend(id, howMany);

        this.lastExecutionTime = System.currentTimeMillis() - startTime;
        //org.webdsl.logging.Logger.info("Obtaining the list of recommendations took: " + this.lastExecutionTime);
        return getObjectsOfIDList(recommendations, RecFieldType.ITEM);
    } catch(NoSuchUserException nse){
        /* Recommendations cannot be given because the user is unknown */
        return new ArrayList<Object>();
    } catch (Exception e){
        org.webdsl.logging.Logger.error("Error, catched an exception while obtaining the recommendations! " + e);
        org.webdsl.logging.Logger.error("EXCEPTION",e);
        return new ArrayList<Object>();
    }
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
public static void main(String[] args) {
    try {
        MysqlDataSource dataSource = new MysqlDataSource();
        dataSource.setServerName("localhost");
        dataSource.setUser("root");
        dataSource.setPassword("root");
        dataSource.setDatabaseName("rec");
        JDBCDataModel dm = new MySQLJDBCDataModel(dataSource,"ratings","userid","itemid","rating","");
        UserSimilarity similarity = new PearsonCorrelationSimilarity(dm);
        UserNeighborhood neighbor = new NearestNUserNeighborhood(2,similarity, dm);
        Recommender recommender = new GenericUserBasedRecommender(dm, neighbor, similarity);
        List<RecommendedItem> list = recommender.recommend(1, 3);// recommend
                                                                 // one item
                                                                 // to user
                                                                 // 1
        for (RecommendedItem ri : list) {
            System.out.println(ri);
        }
    } catch (Exception e) {
        e.printStackTrace();
    }

}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
public static void displayRecommendation(
		List<RecommendedItem> recommendations, DataModel dataModel)
		throws TasteException {

	for (LongPrimitiveIterator users = dataModel.getUserIDs(); users
			.hasNext();) {
		long userId = users.nextLong();
		// List<RecommendedItem> recommendations =
		// recommender.recommend(userId, 5);

		for (RecommendedItem recommendation : recommendations) {
			System.out.println(userId + "," + recommendation.getItemID()
					+ "," + recommendation.getValue());
		}
	}
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
@Override
public RecommendedItem next() {
  FastByIDFloatMap.MapEntry entry = countsIterator.next();
  long id = entry.getKey();
  float value = entry.getValue();
  IDRescorer theRescorer = rescorer;
  if (theRescorer != null) {
    if (theRescorer.isFiltered(id)) {
      return null;
    }
    value = (float) theRescorer.rescore(id, value);
    if (!LangUtils.isFinite(value)) {
      return null;
    }
  }
  delegate.set(id, value);
  return delegate;
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
@Override
public RecommendedItem next() {
  FastByIDMap.MapEntry<float[]> entry = toFeaturesIterator.next();
  long itemID = entry.getKey();
  if (userTagIDs.contains(itemID)) {
    return null;
  }
  float[] candidateFeatures = entry.getValue();
  double candidateFeaturesNorm = SimpleVectorMath.norm(candidateFeatures);
  double estimate = SimpleVectorMath.dot(candidateFeatures, features) / (candidateFeaturesNorm * featuresNorm);
  if (!LangUtils.isFinite(estimate)) {
    return null;
  }
  delegate.set(itemID, (float) estimate);
  return delegate;
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
static String formatOutLine(long id, Iterable<RecommendedItem> recs) {
  StringBuilder result = new StringBuilder(100);
  result.append(id);
  result.append("\t[");
  boolean first = true;
  for (RecommendedItem rec : recs) {
    if (first) {
      first = false;
    } else {
      result.append(',');
    }
    result.append(rec.getItemID()).append(':').append(rec.getValue());
  }
  result.append("]\n");
  return result.toString();
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
@Override
public List<TranslatedRecommendedItem> recommendToAnonymous(String[] itemIDs,
                                                            float[] values,
                                                            int howMany,
                                                            String[] rescorerParams,
                                                            String contextUserID)
    throws TasteException {
  long[] longItemIDs = translateItems(itemIDs);
  Collection<RecommendedItem> originals;
  if (contextUserID == null) {
    originals = delegate.recommendToAnonymous(longItemIDs, values, howMany, rescorerParams, null);
  } else {
    originals =
        delegate.recommendToAnonymous(longItemIDs, values, howMany, rescorerParams, translateUser(contextUserID));
  }
  return translate(originals);
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
private static List<RecommendedItem> consumeItems(URLConnection connection) throws IOException {
  List<RecommendedItem> result = Lists.newArrayList();
  BufferedReader reader = IOUtils.bufferStream(connection.getInputStream());
  try {
    CharSequence line;
    while ((line = reader.readLine()) != null) {
      Iterator<String> tokens = COMMA.split(line).iterator();
      long itemID = Long.parseLong(tokens.next());
      float value = LangUtils.parseFloat(tokens.next());
      result.add(new GenericRecommendedItem(itemID, value));
    }
  } finally {
    reader.close();
  }
  return result;
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
@Test
public void testRecommendToMany() throws Exception {

  ClientRecommender client = getClient();
  // Adding non-existent item to make sure it is ignored
  List<RecommendedItem> recs =
      client.recommendToMany(new long[] {1L, 3L, Integer.MAX_VALUE}, 3, false, (String[]) null);

  assertNotNull(recs);
  assertEquals(3, recs.size());

  log.info("{}", recs);

  assertEquals(286L, recs.get(0).getItemID());
  assertEquals(288L, recs.get(1).getItemID());
  assertEquals(302L, recs.get(2).getItemID());
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
@Test
public void testRecommendToAnonymous() throws Exception {

  ClientRecommender client = getClient();
  // Adding non-existent item to make sure it is ignored
  List<RecommendedItem> recs = client.recommendToAnonymous(new long[] {1L, 3L, Integer.MAX_VALUE}, 3);

  assertNotNull(recs);
  assertEquals(3, recs.size());

  log.info("{}", recs);

  assertEquals(151L, recs.get(0).getItemID());
  assertEquals(50L, recs.get(1).getItemID());
  assertEquals(181L, recs.get(2).getItemID());
}
 

import org.apache.mahout.cf.taste.recommender.RecommendedItem; //导入依赖的package包/类
@Test
public void testMostPopular() throws Exception {

  ClientRecommender client = getClient();
  List<RecommendedItem> popular = client.mostPopularItems(3);

  assertNotNull(popular);
  assertEquals(3, popular.size());

  log.info("{}", popular);

  assertEquals(50L, popular.get(0).getItemID());
  assertEquals(258L, popular.get(1).getItemID());
  assertEquals(100L, popular.get(2).getItemID());
  assertEquals(583.0f, popular.get(0).getValue());
  assertEquals(509.0f, popular.get(1).getValue());
  assertEquals(508.0f, popular.get(2).getValue());
}