Package mia.recommender.ch02

Source Code of mia.recommender.ch02.ItemBaseRecommenderEvaluation

package mia.recommender.ch02;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.*;
import org.apache.mahout.cf.taste.impl.eval.*;
import org.apache.mahout.cf.taste.impl.neighborhood.*;
import org.apache.mahout.cf.taste.impl.recommender.*;
import org.apache.mahout.cf.taste.impl.similarity.*;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import java.io.*;
import java.util.*;
import org.apache.mahout.common.RandomUtils;


/**
  * <p>
  *  This code evaluates a boolean collaborative filtering algorithm.
  * </p>
*/


class ItemBaseRecommenderEvaluation {

  private ItemBaseRecommenderEvaluation() {}
 
  public static void main(String[] args) throws Exception {

  DataModel model = new FileDataModel(new File("ua.base.boolean.csv"));

  RecommenderBuilder builder = new RecommenderBuilder() {
    @Override
    public Recommender buildRecommender(DataModel model) throws TasteException {
      return new GenericBooleanPrefItemBasedRecommender(model, new LogLikelihoodSimilarity(model));
    }
  };

    RecommenderIRStatsEvaluator evaluator = new GenericRecommenderIRStatsEvaluator();
    IRStatistics stats = evaluator.evaluate(builder,
                      null,
                      model,
                      null,
                      1,
                      GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
                      1);

  // on average, about P % of recommendations are good
  System.out.println("PRECISION: On Avarege, about " + stats.getPrecision()*100.0 + "% of recommendations are good" );
 
  // %R of good recommenations are amont those recommended
  System.out.println("RECALL: " + stats.getRecall()*100.0 + "% of good recommenations are among those recommended");

  }


}
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