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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.qualityMeasures;

import cf4j.TestUser;
import cf4j.utils.Methods;

/**
 * This class calculates the Recall of the recommender system. It is calculate as 
 * follows:
 * 
 * recall = <relevant recommended items> / <number of relevant items>
 *
 * This class puts the "Recall" key at the Kernel map containing a double with the 
 * recall value.
 * 
 * @author Fernando Ortega
 */
public class Recall extends QualityMeasure {
	
	private final static String NAME = "Recall";
	
	/**
	 * Number of recommended items
	 */
	private int numberOfRecommendations;
	
	/**
	 * Relevant rating threshold
	 */
	private double relevantThreshold;

	/**
	 * Constructor of Recall
	 * @param numberOfRecommendations Number of recommendations
	 * @param relevantThreshold Minimum rating to consider a rating as relevant
	 */
	public Recall (int numberOfRecommendations, double relevantThreshold) {
		super(NAME);
		this.numberOfRecommendations = numberOfRecommendations;
		this.relevantThreshold = relevantThreshold;
	}

	@Override
	public double getMeasure (TestUser testUser) {
		
		// Items rated as relevant (in test) by the active user		
		int relevant = 0;
		for (double rating : testUser.getTestRatings()) {
			if (rating >= this.relevantThreshold) {
				relevant++;
			}
		}
		
		// Items that has been recommended and was relevant to the active user
		double [] predictions = testUser.getPredictions();
		int [] recommendations = Methods.findTopN(predictions, this.numberOfRecommendations);
		
		int recommendedAndRelevant = 0;

		for (int testItemIndex : recommendations) {
			if (testItemIndex == -1) break;
			
			if (testUser.getTestRatings()[testItemIndex] >= this.relevantThreshold) {
				recommendedAndRelevant++;
			}			
		}
		
		double recall = (double) recommendedAndRelevant / (double) relevant;
		return recall;
	}
}