Mahout: GenericItemBasedRecommender

GenericItemBasedRecommender uses DataModel and ItemSimilarity to produce recommendations. Itembased recommenders generate recommendations based on item similarity, not user similarity, and item similarity is relatively static. It can be precomputed, instead of re-computed in real time.

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.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;
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 GenericItemBasedRecommenderEx {
 private static String input = "/Users/harikrishna_gurram/customer.csv";
 private static DataModel model = null;
 private static LogLikelihoodSimilarity 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 LogLikelihoodSimilarity(model);

  recommender = new GenericItemBasedRecommender(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
7 A short-form master 3.484951
6 The 60s kids classic 3.1145873
3 Memoir as metafiction 3.0694165
8 Go down the rabbit hole 2.7363687
*************************************************

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