Generating Recommendations with mahout for Boolean data sets

June 27, 2016 by S4

Filed under Hadoop, Mahout

Last modified June 27, 2016

Generating Recommendations with mahout for Boolean data sets (data sets with no preference value)

Boolean data Sets
Input data set that doesn’t have a preference value, ie input data set would be of the format UserId1,ItemId1
UserId2,ItemId2
Here it’d based on some data where an user either likes an item or he doesn’t, there is no preference value associated with this.

When we use Boolean data sets we need to appropriately choose the Similarity algorithms and Recommenders

Similarity Algorithms
For Boolean data sets we can either go in for Tanimoto Coefficient Similarity or Log Likelihood Similarity

Recommender
We need to use Generic Boolean Pref User Based Recommender or Generic Boolean Pref Item Based Recommender

Sample codes for generating User based and Item Based recommendations are given below

Used Based Recommender for Boolean Data Sets

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.neighborhood.ThresholdUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericBooleanPrefUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;

public class UserRecommender {

public static void main(String args[])
{
// specifying the user id to which the recommendations have to be generated for
int userId=510;

//specifying the number of recommendations to be generated
int noOfRecommendations=5;

//specifying theNeighborhood size
double thresholdValue=0.7;

try
{
// Data model created to accept the input file
FileDataModel dataModel = new FileDataModel(new File(“D://input.txt”));

/*TanimotoCoefficientSimilarity is intended for “binary” data sets
where a user either expresses a generic “yes” preference for an item or has no preference.*/
UserSimilarity userSimilarity = new TanimotoCoefficientSimilarity(dataModel);

/*ThresholdUserNeighborhood is preferred in situations where we go in for a
similarity measure between neighbors and not any number*/
UserNeighborhood neighborhood =new ThresholdUserNeighborhood(thresholdValue, userSimilarity, dataModel);

/*GenericBooleanPrefUserBasedRecommender is appropriate for use when no notion
of preference value exists in the data. */
Recommender recommender =new GenericBooleanPrefUserBasedRecommender(dataModel, neighborhood, userSimilarity);

//calling the recommend method to generate recommendations
List recommendations =recommender.recommend(userId, noOfRecommendations);

//
for (RecommendedItem recommendedItem : recommendations)
System.out.println(recommendedItem.getItemID());
}
catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (TasteException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}

}

}

Item Based Recommender for Boolean Data Sets

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.recommender.ItemBasedRecommender;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;

public class ItemRecommender {

public static void main(String args[])
{
// specifying the user id to which the recommendations have to be generated for
int userId=510;

//specifying the number of recommendations to be generated
int noOfRecommendations=5;

try
{
// Data model created to accept the input file
FileDataModel dataModel = new FileDataModel(new File(“D://input.txt”));

/*Specifies the Similarity algorithm*/
ItemSimilarity itemSimilarity = new LogLikelihoodSimilarity(dataModel);

/*Initalizing the recommender */
ItemBasedRecommender recommender =new GenericItemBasedRecommender(dataModel, itemSimilarity);

//calling the recommend method to generate recommendations
List recommendations =recommender.recommend(userId, noOfRecommendations);

//
for (RecommendedItem recommendedItem : recommendations)
System.out.println(recommendedItem.getItemID());
}
catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (TasteException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}

}
}

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