Mahout: Cluster based recommendations

In cluster based recommendation, items are recommended to clusters of users. In cluster based recommendation, users are grouped into clusters, next recommendations are produced for each cluster. This kind of approach is very effective for new users. Suppose if a new user comes to your web site, you had least amount of data about the user(Since he is new user), In this case, you just map this user to a cluster and recommend items.

Mahout provides following recommenders to work with clusters.

TreeClusteringRecommender

TreeClusteringRecommender2

To work with above recommenders, first we need to construct an instace for ClusterSimilarity.

ClusterSimilarity returns the "similarity" between two clusters of users. Following classes implements ClusterSimilarity interface.

FarthestNeighborClusterSimilarity: Defines cluster similarity as the smallest similarity between any two users in the clusters -- that is, it says that clusters are close when all pairs of their members have relatively high similarity.

NearestNeighborClusterSimilarity: Defines cluster similarity as the largest similarity between any two users in the clusters -- that is, it says that clusters are close when some pair of their members has high similarity.

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.NearestNeighborClusterSimilarity;
import org.apache.mahout.cf.taste.impl.recommender.TreeClusteringRecommender;
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;

public class TreeClusteringRecommenderEx {
 private static String input = "/Users/harikrishna_gurram/customer.csv";
 private static DataModel model = null;
 private static LogLikelihoodSimilarity similarity = null;
 private static TreeClusteringRecommender 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);

  NearestNeighborClusterSimilarity clusterSimilarity = new NearestNeighborClusterSimilarity(
    similarity);

  recommender = new TreeClusteringRecommender(model, clusterSimilarity, 5);

  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 3.4
3 Memoir as metafiction 2.6
6 The 60s kids classic 2.5
8 Go down the rabbit hole 2.0
*************************************************

Following are the deprecated recommenders, so i am not going to explain much about these.

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/ClusterSimilarity.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/FarthestNeighborClusterSimilarity.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/NearestNeighborClusterSimilarity.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/TreeClusteringRecommender2.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/knn/ConjugateGradientOptimizer.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/knn/KnnItemBasedRecommender.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/knn/NonNegativeQuadraticOptimizer.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/knn/Optimizer.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/svd/FunkSVDFactorizer.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/svd/ImplicitLinearRegressionFactorizer.java

/mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/recommender/ClusteringRecommender.java

Go through following link for more details on why these are deprecated.

https://issues.apache.org/jira/browse/MAHOUT-1124

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