Mahout: KnnItemBasedRecommender

Knn stands for K nearest neighbors. The weights to compute the final predicted preferences are calculated using linear interpolation, through an Optimizer. This algorithm is based in the paper of Robert M. Bell and Yehuda Koren in ICDM '07.

KnnItemBasedRecommender class provides following constructors instantiate.

KnnItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity, Optimizer optimizer, CandidateItemsStrategy candidateItemsStrategy, MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy, int neighborhoodSize)

          

KnnItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity, Optimizer optimizer, int neighborhoodSize)

Following classes implements Optimizer interface.

NonNegativeQuadraticOptimizer

ConjugateGradientOptimizer

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 4.

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.knn.ConjugateGradientOptimizer;
import org.apache.mahout.cf.taste.impl.recommender.knn.KnnItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.knn.Optimizer;
import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;

public class KnnItemBasedRecommenderEx {
 private static String input = "/Users/harikrishna_gurram/customer.csv";
 private static DataModel model = null;
 private static KnnItemBasedRecommender recommender = null;
 private static Optimizer optimizer = null;
 private static ItemSimilarity similarity = 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);
  optimizer = new ConjugateGradientOptimizer();
  recommender = new KnnItemBasedRecommender(model, similarity, optimizer,
    10);

  List<RecommendedItem> recommendations = recommender.recommend(4, 5);

  System.out.println("Recommendations for customer " + userNames[3]
    + " 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 Sudheer Ganji are:
*************************************************
BookId itle  estimated preference
3 Memoir as metafiction NaN
*************************************************

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