Recommendation Engine in Mahout

User Based and Item Based

Mahout is a collection of machine learning algorithms intended to perform the following operations as recommendation (Collaborative Filtering), Clustering and Classification. Initially to implement recommendation we need an input data file where every line contains one record each. Each record should have the user ID, Item ID and preference value in order separated by comma.

Input File – input.txt

501,1002,5

501,1012,3

510,1002,2

515,1002,5

501,1020,1

…

                The point to be considered here that we need the User Id and Item ID to be integers, alpha numeric characters won’t serve our purpose. Also the larger the input files better the quality of recommendations produced

Recommenders

                Recommenders are broadly classified into two categories based on the method or approach they use in generating recommendations

1.       User Based Recommendations

                Recommendations are derived from how similar items are to items, ie based on the items a user has already more similar items are recommended

2.       Item Based Recommendations

                Recommendations are derived on how similar users to users are. ie to make recommendations for a user(User1) we take into account an user/users who shares similar tastes and based on the items they possess we recommend items to User1

                When we make mahout recommendations the key components involved are

Data Model

                It is an encapsulation used by Mahout to hold input data. It helps efficient access to data by various recommender algorithms.

Similarity Algorithm

                There are various kind of Similarity algorithms available and mahout has implementations of all the popular ones like Person Correlation, Cosine Measure, Euclidean Distance, Log Likelihood, Tanimoto coefficient etc

User Neighborhood

                This is applicable for user based recommendations, user based recommendations are made based on user to user similarity. We form a neighborhood of most similar users that share almost same tastes so that we get better recommendations. And the algorithms thet we use to select user neighborhood are

1.       Nearest N User Neighborhood

                Here we specify the neighborhood size, ie exactly the number of most similar uses to be considered for generating recommendations say 100,500 etc

2.       Threshold User Neighborhood

                We don’t specify the neighborhood size, rather we specify a similarity measure which is a value between -1 and +1. If we specify a value .7 then only the users that share a similarity greater than ).7 would be considered in neighborhood. Higher the value more similar the users are

Recommender

                It is the final computing object which couples together the datamodel, similarity algorithm and neighborhood to generate recommendations based on the same

                Samples code snippets to generate user and item based recommendations are given below

User Based Recommender

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.NearestNUserNeighborhood;

import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;

import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;

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;

           

            try

            {

                  // Data model created to accept the input file

                  FileDataModel dataModel = new FileDataModel(new File("D://input.txt"));

                 

                  /*Specifies the Similarity algorithm*/

                  UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel);

                 

                  /*NearestNUserNeighborhood is preferred in situations where we need to have control on the exact no of neighbors*/

                  UserNeighborhood neighborhood =new NearestNUserNeighborhood(100, userSimilarity, dataModel);

                 

                  /*Initalizing the recommender */

                  Recommender recommender =new GenericUserBasedRecommender(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

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.PearsonCorrelationSimilarity;

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 PearsonCorrelationSimilarity(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: To get some recommendations you a sufficiently large input file. A few lines of input won’t gain you any recommendations