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<RecommendedItem> 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<RecommendedItem> 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();

            }

           

      }

}

Note: For more details on basic recommendations refer generating recommendations with mahout