SVD stands for singular value decomposition. It is a method for matrix decomposition factorization. Singular value decomposition mainly used in signal processing and statistics.
You can go through following wiki article, to get more information.
https://en.wikipedia.org/wiki/Singular_value_decomposition
You can initialize SVDRecommender using Factorizier.
Following are the implementations for Factorizer class.
a. ALSWRFactorizer (factorizes the rating matrix using "Alternating-Least-Squares with Weighted-λ-Regularization").
b. ExpectationMaximizationSVDFactorizer (Calculates the SVD using an Expectation Maximization algorithm).
c. ImplicitLinearRegressionFactorizer
Let’s say I had following input data.
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Book id
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Title
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1
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Meet Big Brother
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2
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Explore the Universe
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3
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Memoir as metafiction
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4
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A child-soldier's story
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5
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Wicked good fun
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6
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The 60s kids classic
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7
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A short-form master
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8
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Go down the rabbit hole
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9
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Unseated a president
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10
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An Irish-American Memoir
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User id
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Name
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1
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Hari Krishna Gurram
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2
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Gopi Battu
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3
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Rama Krishna Gurram
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4
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Sudheer Ganji
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5
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Kiran Darsi
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6
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Joel Chelli
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7
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Sankalp Dubey
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8
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Sunil Kumar
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9
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Janaki Sriram
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10
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Phalgun Garimella
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11
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Reshmi George
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12
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Sailaja Navakotla
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13
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Aravind Phaneendra
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14
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Keerthi Shetty
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15
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Sujatha
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16
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Vadiraj Kulakarni
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17
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Arpan
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18
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Suprabath Bisoi
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19
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Sravani
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20
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Gireesh Amara
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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.svd.ImplicitLinearRegressionFactorizer; import org.apache.mahout.cf.taste.impl.recommender.svd.SVDRecommender; import org.apache.mahout.cf.taste.model.DataModel; import org.apache.mahout.cf.taste.recommender.RecommendedItem; public class SVDRecommenderEx { private static String input = "/Users/harikrishna_gurram/customer.csv"; private static DataModel model = null; private static SVDRecommender recommender = null; private static ImplicitLinearRegressionFactorizer fatorizer = 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)); fatorizer = new ImplicitLinearRegressionFactorizer(model); recommender = new SVDRecommender(model, fatorizer); Listrecommendations = 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 3.3652906 7 A short-form master 0.87520653 8 Go down the rabbit hole 0.19163734 3 Memoir as metafiction -1.8170359 *************************************************