Mahout: ItemAverageRecommender

It estimates preference for an item to be the average of all known preference values for that item. It will not consider user information, so it is used for experimentation only. It is easy to implement and works very fast, but may not produce good 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.

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.ItemAverageRecommender;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;

public class ItemAverageRecommenderEx {
 private static String input = "/Users/harikrishna_gurram/customer.csv";
 private static DataModel model = null;
 private static ItemAverageRecommender 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));

  recommender = new ItemAverageRecommender(model);

  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
6 The 60s kids classic 3.1111112
7 A short-form master 2.5
8 Go down the rabbit hole 2.4
3 Memoir as metafiction 2.2666667
*************************************************

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