Tuesday 15 September 2015

Mahout: GenericUserBasedRecommender

It uses DataModel, UserNeighborhood, UserSimilarity to generate recommendations.

GenericUserBasedRecommender(DataModel dataModel, UserNeighborhood neighborhood, UserSimilarity similarity)

Let’s say I had following input data.

Book id
Title
1
Meet Big Brother
2
Explore the Universe
3
Memoir as metafiction
4
A child-soldier's story
5
Wicked good fun
6
The 60s kids classic
7
A short-form master
8
Go down the rabbit hole
9
Unseated a president
10
An Irish-American Memoir

User id
Name
1
Hari Krishna Gurram
2
Gopi Battu
3
Rama Krishna Gurram
4
Sudheer Ganji
5
Kiran Darsi
6
Joel Chelli
7
Sankalp Dubey
8
Sunil Kumar
9
Janaki Sriram
10
Phalgun Garimella
11
Reshmi George
12
Sailaja Navakotla
13
Aravind Phaneendra
14
Keerthi Shetty
15
Sujatha
16
Vadiraj Kulakarni
17
Arpan
18
Suprabath Bisoi
19
Sravani
20
Gireesh Amara

Following csv file contains customers purchages and their ratings on books.


customer.csv
1,1,3
1,2,1
1,4,5
1,5,3
1,9,3
1,10,2
2,1,2
2,3,2
2,4,1
2,7,5
3,1,5
3,2,1
3,3,1
3,6,1
3,8,1
4,1,1
4,2,1
4,6,3
4,7,1
4,9,2
5,2,1
5,3,3
5,6,5
5,10,3
6,1,1
6,2,4
6,3,4
6,7,2
6,8,3
7,1,3
7,2,3
7,3,1
7,5,3
7,6,3
7,7,3
8,1,1
8,3,3
8,4,5
8,8,1
8,9,2
9,4,2
9,6,5
9,8,3
9,9,3
10,2,5
10,3,1
10,4,2
10,5,1
10,9,4
11,2,3
11,4,2
11,5,2
11,8,1
12,1,1
12,3,4
12,7,3
12,8,2
13,1,3
13,2,4
13,3,2
13,5,3
13,9,3
14,2,3
14,3,2
14,5,1
14,7,1
14,8,5
14,9,2
15,1,3
15,2,2
15,3,2
15,6,5
15,7,1
15,9,3
16,2,2
16,3,4
16,6,1
16,7,3
16,10,1
17,3,1
17,4,3
17,7,4
17,8,4
18,3,3
18,5,2
18,6,3
18,9,1
18,10,2
19,1,1
19,2,5
19,6,2
19,7,2
19,8,3
19,10,3
20,1,2
20,2,2
20,3,1
20,4,4
20,8,1

20,8,1 means User20 liked item8 and given rating 1.


Following application finds recommendations, similar users for customer 1.
import java.io.File;
import java.io.IOException;
import java.util.List;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
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.LogLikelihoodSimilarity;
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.UserBasedRecommender;

public class GenericUserBasedRecommenderEx {
 private static String input = "/Users/harikrishna_gurram/customer.csv";
 private static final int NEIGHBORHOOD_SIZE = 5;
 private static DataModel model = null;
 private static LogLikelihoodSimilarity similarity = null;
 private static UserNeighborhood neighborhood = null;
 private static UserBasedRecommender recommender = null;

 private static String[] books = { "Meet Big Brother",
   "Explore the Universe", "Memoir as metafiction",
   "A child-soldier's story", "Wicked good fun",
   "The 60s kids classic", "A short-form master",
   "Go down the rabbit hole", "Unseated a president",
   "An Irish-American Memoir" };

 private static String[] userNames = { "Hari Krishna Gurram", "Gopi Battu",
   "Rama Krishna Gurram", "Sudheer Ganji", "Kiran Darsi",
   "Joel Chelli", "Sankalp Dubey", "Sunil Kumar", "Janaki Sriram",
   "Phalgun Garimella", "Reshmi george", "Sailaja Navakotla",
   "Aravind Phaneendra", "Keerthi Shetty", "Sujatha",
   "Vadiraj Kulakarni", "Arpan", "Suprabath Bisoi", "Sravani",
   "Gireesh Amara" };

 public static void main(String args[]) throws IOException, TasteException {
  model = new FileDataModel(new File(input));
  similarity = new LogLikelihoodSimilarity(model);
  neighborhood = new NearestNUserNeighborhood(NEIGHBORHOOD_SIZE,
    similarity, model);

  recommender = new GenericUserBasedRecommender(model, neighborhood,
    similarity);

  List<RecommendedItem> recommendations = recommender.recommend(1, 5);

  System.out.println("Recommendations for customer " + userNames[0]
    + " are:");
  System.out.println("*************************************************");

  System.out.println("BookId\title\t\testimated preference");
  for (RecommendedItem recommendation : recommendations) {
   int bookId = (int) recommendation.getItemID();
   float estimatedPref = recommender.estimatePreference(1, bookId);
   System.out.println(bookId + " " + books[bookId - 1] + "\t"
     + estimatedPref);
  }

  System.out.println("*************************************************");

  long[] userIds = recommender.mostSimilarUserIDs(1, 5);
  System.out.println("Most similar users for " + userNames[0] + " are");
  for (long id : userIds) {
   System.out.println(id + " " + userNames[(int) id - 1]);
  }

 }
}


Output

Recommendations for customer Hari Krishna Gurram are:
*************************************************
BookId itle  estimated preference
7 A short-form master 3.067125
8 Go down the rabbit hole 2.552092
3 Memoir as metafiction 2.2255363
*************************************************
Most similar users for Hari Krishna Gurram are
12 Sailaja Navakotla
17 Arpan
3 Rama Krishna Gurram
6 Joel Chelli
10 Phalgun Garimella



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