Tuesday 15 September 2015

Mahout : GenericBooleanPrefUserBasedRecommender

Sometimes you need to generate recommendations for input data, which has no preference values. What I mean is, data should be like following. Even if you provide preference values these are simply ignored.

User_id1 item_id1
User_id2 item_id2
User_id3 item_id3
User_id4 item_id4

Above kind of data is called Boolean preference data, since it has no preference value. To handle such kind of data, we need to select proper similarity algorithms and recommenders.

Choosing similarity algorithm
For this example, i am going to use TanimotoCoefficientSimilarity, is intended for "binary" data sets (preference value doesn't matter here).

Choose Recommender
I am going to use GenericBooleanPrefUserBasedRecommender here. (You can use GenericBooleanPrefItemBasedRecommender, for item based recommendations).

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.GenericBooleanPrefUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
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;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;

public class GenericBooleanPrefUserBasedRecommenderEx {
 private static String input = "/Users/harikrishna_gurram/customer.csv";
 private static final int NEIGHBORHOOD_SIZE = 5;
 private static DataModel model = null;
 private static UserSimilarity 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 TanimotoCoefficientSimilarity(model);
  neighborhood = new NearestNUserNeighborhood(NEIGHBORHOOD_SIZE,
    similarity, model);

  recommender = new GenericBooleanPrefUserBasedRecommender(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
3 Memoir as metafiction 1.5178572
8 Go down the rabbit hole 0.80357146
6 The 60s kids classic 0.375
7 A short-form master 0.375
*************************************************
Most similar users for Hari Krishna Gurram are
10 Phalgun Garimella
13 Aravind Phaneendra
11 Reshmi george
4 Sudheer Ganji
8 Sunil Kumar



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