GroupLens10mRatingsItemRecommenderEvaluator.java

package org.mahout.recommendations;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.common.Weighting;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;
import org.apache.mahout.cf.taste.example.grouplens.GroupLensDataModel;
import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;

import java.io.File;

/**
 * @author Krisztian Horvath
 */
public class GroupLens10mRatingsItemRecommenderEvaluator {

    public static void main(String[] args) throws Exception {
        DataModel model = new GroupLensDataModel(new File("src/main/resources/ratings.dat"));
        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
        RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
            @Override
            public Recommender buildRecommender(DataModel model) throws TasteException {
                ItemSimilarity similarity = new PearsonCorrelationSimilarity(model, Weighting.WEIGHTED);
                return new GenericItemBasedRecommender(model, similarity);
            }
        };
        double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.05);
        System.out.println(score);
    }
}