uit.tkorg.pr.method.cf.SVDCF.java Source code

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Here is the source code for uit.tkorg.pr.method.cf.SVDCF.java

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package uit.tkorg.pr.method.cf;

import java.io.BufferedWriter;
import java.io.File;
import java.io.FileWriter;
import java.io.IOException;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import org.apache.commons.io.FileUtils;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.impl.model.file.*;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.svd.*;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.impl.similarity.UncenteredCosineSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import uit.tkorg.pr.method.GenericRecommender;
import uit.tkorg.pr.model.Author;

/**
 *
 * @author Minh
 */
public class SVDCF {

    /**
     *
     * @param inputFile
     * @param n
     * @param numFeatures
     * @param lamda
     * @param numIterations
     * @param outputFile
     * @throws IOException
     * @throws TasteException
     */
    public static void SVDRecommendation(String inputFile, int n, int numFeatures, double lamda, int numIterations,
            String outputFile) throws IOException, TasteException {
        DataModel dataModel = new FileDataModel(new File(inputFile));
        Factorizer factorizer = new ALSWRFactorizer(dataModel, numFeatures, lamda, numIterations);
        Recommender svdRecommender = new SVDRecommender(dataModel, factorizer);
        BufferedWriter bw = new BufferedWriter(new FileWriter(outputFile));

        // Recommend n items for each user
        for (LongPrimitiveIterator iterator = dataModel.getUserIDs(); iterator.hasNext();) {
            long userId = iterator.nextLong();

            // Generate a list of n recommendations for the user
            List<RecommendedItem> topItems = svdRecommender.recommend(userId, n);
            if (!topItems.isEmpty()) {
                // Display the list of recommendations
                for (RecommendedItem recommendedItem : topItems) {
                    bw.write(
                            userId + "," + recommendedItem.getItemID() + "," + recommendedItem.getValue() + "\r\n");
                }
            }
        }
        bw.close();
    }

    public static void computeCFRatingAndPutIntoModelForAuthorList(String inputFile, int numFeatures, double lamda,
            int numIterations, HashMap<String, Author> authorTestSet, HashSet<String> paperIdsInTestSet,
            String outputFile) throws IOException, TasteException {
        DataModel dataModel = new FileDataModel(new File(inputFile));
        Factorizer factorizer = new ALSWRFactorizer(dataModel, numFeatures, lamda, numIterations);

        Recommender svdRecommender = new SVDRecommender(dataModel, factorizer);

        FileUtils.deleteQuietly(new File(outputFile));
        try (BufferedWriter bw = new BufferedWriter(new FileWriter(outputFile))) {
            int count = 0;
            System.out.println("Number of users:" + authorTestSet.size());
            for (LongPrimitiveIterator iterator = dataModel.getUserIDs(); iterator.hasNext();) {
                long userId = iterator.nextLong();
                // Generate a list of n recommendations for the user
                if (authorTestSet.containsKey(String.valueOf(userId).trim())) {
                    System.out.println("Computing CF rating value for user no. " + count);
                    List<RecommendedItem> recommendationList = svdRecommender.recommend(userId,
                            dataModel.getNumItems());
                    if (!recommendationList.isEmpty()) {
                        // Display the list of recommendations
                        for (RecommendedItem recommendedItem : recommendationList) {
                            String authorId = String.valueOf(userId).trim();
                            String paperId = String.valueOf(recommendedItem.getItemID()).trim();
                            if (paperIdsInTestSet.contains(paperId)) {
                                authorTestSet.get(authorId).getCfRatingHM().put(paperId,
                                        Float.valueOf(recommendedItem.getValue()));
                                bw.write(userId + "," + recommendedItem.getItemID() + ","
                                        + recommendedItem.getValue() + "\r\n");
                            }
                        }
                    }
                    count++;
                }
            }
        }
    }
}