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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.similarities;

import cf4j.Item;
import cf4j.Kernel;
import cf4j.TestItem;
import cf4j.User;

/**
 * Implements traditional Ajusted Cosine as CF similarity metric for the items.
 * 
 * @author Fernando Ortega
 */
public class MetricAjustedCosine extends ItemsSimilarities {

	@Override
	public double similarity (TestItem activeItem, Item targetItem) {
		
		int u = 0, v = 0, common = 0; 
		double num = 0d, denActive = 0d, denTarget = 0d;
		
		while (u < activeItem.getNumberOfRatings() && v < targetItem.getNumberOfRatings()) {
			if (activeItem.getUsers()[u] < targetItem.getUsers()[v]) {
				u++;
			} else if (activeItem.getUsers()[u] > targetItem.getUsers()[v]) {
				v++;
			} else {
				int userCode = activeItem.getUsers()[u];
				User user = Kernel.gi().getUserByCode(userCode);
				double avg = user.getRatingAverage();
				
				double fa = activeItem.getRatings()[u] - avg;
				double ft = targetItem.getRatings()[v] - avg;
				
				num += fa * ft;
				denActive += fa * fa;
				denTarget += ft * ft;
				
				common++;
				u++; 
				v++;
			}	
		}

		// If there is not ratings in common, similarity does not exists
		if (common == 0) return Double.NEGATIVE_INFINITY;

		// Denominator can not be zero
		if (denActive == 0 || denTarget == 0) return Double.NEGATIVE_INFINITY;

		// Return similarity
		return num / Math.sqrt(denActive * denTarget);
	}
}