Mahout GenericBooleanPrefItemBasedRecommender

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 GenericBooleanPrefItemBasedRecommender here. (You can use GenericBooleanPrefUserBasedRecommender, for user 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 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.recommender.GenericBooleanPrefItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.ItemBasedRecommender;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;

public class GenericBooleanPrefItemBasedRecommenderEx {
 private static String input = "/Users/harikrishna_gurram/customer.csv";
 private static DataModel model = null;
 private static TanimotoCoefficientSimilarity similarity = null;
 private static ItemBasedRecommender 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);

  recommender = new GenericBooleanPrefItemBasedRecommender(model,
    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("*************************************************");

 }
}

Output

Recommendations for customer Hari Krishna Gurram are:
*************************************************
BookId itle  estimated preference
3 Memoir as metafiction 2.108015
6 The 60s kids classic 1.6410714
8 Go down the rabbit hole 1.4852107
7 A short-form master 1.4781109
*************************************************

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