Dummy Mahout Recommender System Example

I already talked about the Open Source Apache Mahout here, and now I'll show a dummy dummy first example of how to use its recommender system.

It is a basic Java example that I used to try out Mahout. Hope it helps people starting to work with it.

 

package myexample;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.XmlFile;
import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;
import org.apache.mahout.cf.taste.impl.recommender.slopeone.SlopeOneRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender;
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.similarity.AveragingPreferenceInferrer;

import org.xml.sax.InputSource;
import org.xml.sax.SAXException;
import javax.xml.parsers.ParserConfigurationException;
import javax.xml.parsers.SAXParser;
import javax.xml.parsers.SAXParserFactory;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.List;

/*
 * Renata Ghisloti - Dummy Mahout Example
 */


public class GeneralRecommender {

  public static void main(String[] args) throws IOException, TasteException, SAXException, ParserConfigurationException {

    String recsFile = args[0];
    long userId = Long.parseLong(args[1]);
    String categoriesFile = args[2];
    String outputPlace = args[3];
    Integer neighborhoodSize = Integer.parseInt(args[4]);
    Integer method = 0;
    String version = null;

    if(args.length >= 6 )
    {
        method  = Integer.parseInt(args[5]);
        version = args[6];
    }

    //Default - needed to initiate the recommendation
    InputSource is = new InputSource(new FileInputStream(recsFile));
    SAXParserFactory factory = SAXParserFactory.newInstance();
    factory.setValidating(false);
    SAXParser sp = factory.newSAXParser();
    ContentHandler handler = new ContentHandler();
    sp.parse(is, handler);

    //Here is were you should load your own input
    XmlFile dataModel = new XmlFile(new File(recsFile));

    switch(method){
      case 0:
       recommenderItemBased(dataModel, userId , categoriesFile, outputPlace, handler, version);
       break;
      case 1:
       recommenderItemBased(dataModel, userId , categoriesFile, outputPlace, handler, version);
       break;
      case 2:
       recommenderSlopeOne(dataModel, userId , categoriesFile, outputPlace, handler);
       break;
      case 3:
       recommenderUserBased(dataModel, userId , categoriesFile, outputPlace, handler, neighborhoodSize, version);
       break;
    }
  }

    //Item Based Recommender System
    public static void recommenderItemBased(XmlFile dataModel, long userId ,
        String categoriesFile, String outputPlace, ContentHandler handler, String version) throws  TasteException{

        System.out.println("Recommending with Item Based");
        ItemSimilarity itemSimilarity;

        if(version == "LogLikelihoodSimilarity")
            itemSimilarity = new LogLikelihoodSimilarity(dataModel);
        else {
            itemSimilarity = new PearsonCorrelationSimilarity(dataModel); 
            System.out.println("Recommending with Item Based Pearson");
        }
        ItemBasedRecommender recommender =
            new GenericItemBasedRecommender(dataModel, itemSimilarity);

        //Just get top 5 recommendations
        List recommendations =
            recommender.recommend(userId, 5);

        //This is were you should add your own print output method
        PrintXml.printRecs(dataModel, userId, recommendations, handler.map, categoriesFile, outputPlace);
    }


    //Slope One Recommender System
    public static void recommenderSlopeOne(XmlFile dataModel, long userId ,
        String categoriesFile, String outputPlace, ContentHandler handler) throws  TasteException{

        System.out.println("Recommending with Slope One");

        CachingRecommender cachingRecommender = new CachingRecommender(new SlopeOneRecommender(dataModel));

        List recommendations =
            cachingRecommender.recommend(userId, 5);

        PrintXml.printRecs(dataModel, userId, recommendations, handler.map, categoriesFile, outputPlace);
    }


    //User based Recommender System
    public static void recommenderUserBased(XmlFile dataModel, long userId ,
        String categoriesFile, String outputPlace, ContentHandler handler, Integer neighborhoodSize, String version) throws  TasteException{

        System.out.println("Recommending with User Based");
        UserSimilarity userSimilarity;

        if(version == "LogLikelihoodSimilarity")
            userSimilarity = new LogLikelihoodSimilarity(dataModel);
        else
            userSimilarity = new PearsonCorrelationSimilarity(dataModel);

        userSimilarity.setPreferenceInferrer(new AveragingPreferenceInferrer(dataModel));

        UserNeighborhood neighborhood =
            new NearestNUserNeighborhood(neighborhoodSize, userSimilarity, dataModel);

        Recommender recommender =
            new GenericUserBasedRecommender(dataModel, neighborhood, userSimilarity);

        List recommendations =
            recommender.recommend(userId, 5);

    PrintXml.printRecs(dataModel, userId, recommendations, handler.map, categoriesFile, outputPlace);
    }
}