org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity Java Examples

The following examples show how to use org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: MovieUserRecommender.java    From hiped2 with Apache License 2.0 6 votes vote down vote up 
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());
    }
  }
}
Example #2
Source File: UserbasedRecommender.java    From Building-Recommendation-Engines with MIT License 5 votes vote down vote up 
public static void main( String[] args ) throws IOException, TasteException
{
	//user based recommender model
	DataModel model = new FileDataModel(new File("data/dataset.csv"));    	
	UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
	UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
	UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
	List<RecommendedItem> recommendations = recommender.recommend(2, 3);
	for (RecommendedItem recommendation : recommendations) {
	  System.out.println(recommendation);
	}
}
Example #3
Source File: BookRecommender.java    From Machine-Learning-in-Java with MIT License 5 votes vote down vote up 
public static ItemBasedRecommender itemBased() throws Exception {

		// Load the data
		StringItemIdFileDataModel dataModel = loadFromFile("data/BX-Book-Ratings.csv", ";");
		// Collection<GenericItemSimilarity.ItemItemSimilarity> correlations =
		// null;
		// ItemItemSimilarity iis = new ItemItemSimilarity(0, 0, 0);
		// ItemSimilarity itemSimilarity = new
		// GenericItemSimilarity(correlations);
		ItemSimilarity itemSimilarity = new PearsonCorrelationSimilarity(dataModel);

		ItemBasedRecommender recommender = new GenericItemBasedRecommender(
				dataModel, itemSimilarity);

		IDRescorer rescorer = new MyRescorer();

		// List recommendations = recommender.recommend(2, 3, rescorer);
		String itemISBN = "042513976X";
		long itemID = dataModel.readItemIDFromString(itemISBN);
		int noItems = 10;

		System.out.println("Recommendations for item: " + books.get(itemISBN));

		System.out.println("\nMost similar items:");
		List<RecommendedItem> recommendations = recommender.mostSimilarItems(
				itemID, noItems);
		for (RecommendedItem item : recommendations) {
			itemISBN = dataModel.getItemIDAsString(item.getItemID());
			System.out.println("Item: " + books.get(itemISBN) + " | Item id: "
					+ itemISBN + " | Value: " + item.getValue());
		}
		
		return recommender;
	}
Example #4
Source File: MovieUserEvaluator.java    From hiped2 with Apache License 2.0 5 votes vote down vote up 
@Override
public Recommender buildRecommender(DataModel model)
    throws TasteException {
  UserSimilarity similarity =
      new PearsonCorrelationSimilarity(model);

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

  return new GenericUserBasedRecommender(
      model, neighborhood, similarity);
}