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A Java's Collaborative Filtering library to carry out experiments in research of Collaborative Filtering based Recommender Systems. The library has been designed from researchers to researchers.

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package cf4j.knn.itemToItem.aggreagationApproaches;

import cf4j.Item;
import cf4j.Kernel;
import cf4j.TestItem;
import cf4j.TestUser;
import cf4j.TestUsersPartible;

/**
 * This class computes the prediction of the test users' test items. The results are 
 * saved in double array on the hashmap of each test user with the key "predictions". This 
 * array overlaps with the test items' array of the test users. For example, the prediction
 * retrieved with the method testUser.getPredictions()[i] is the prediction of the item
 * testUser.getTestItems()[i].
 * 
 * This class uses standard average as method to combine the ratings of the items more
 * similar to the active one.
 * 
 * @author Fernando Ortega
 */
public class Mean implements TestUsersPartible {

	@Override
	public void beforeRun() { }

	@Override
	public void run (int testUserIndex) {

		TestUser testUser = Kernel.gi().getTestUsers()[testUserIndex];
				
		int numRatings = testUser.getNumberOfTestRatings();
		double [] predictions = new double [numRatings];
		
		for (int testItemIndex = 0; testItemIndex < numRatings; testItemIndex++) {
			
			int itemCode = testUser.getTestItems()[testItemIndex];
			TestItem item = Kernel.gi().getTestItemByCode(itemCode);
			
			int [] neighbors = item.getNeighbors();
			int count = 0;
			
			for (int n = 0; n < neighbors.length; n++) {
				if (neighbors[n] == -1) break; // Neighbors array are filled with -1 when no more neighbors exists
				
				int itemIndex = neighbors[n];
				Item neighbor = Kernel.gi().getItems()[itemIndex];
				int neighborCode = neighbor.getItemCode();
								
				int i = testUser.getItemIndex(neighborCode);
				if (i != -1) {
					predictions[testItemIndex] += testUser.getRatings()[i];
					count++;
				}
			}
			
			predictions[testItemIndex] = (count == 0) ? Double.NaN : predictions[testItemIndex] / count;
		}

		testUser.setPredictions(predictions);
	}

	@Override
	public void afterRun() { }
}