mahout做推薦的輸入只能是long型別，但在某些網站中，儲存的資料不是long型別，是string型別。
現在的手機APP，每個手機都有其device_id，也是string型別。如果能以string型別作為uid，即使使用者不註冊，不登入。只要採用device_id作為其uid，也可以做精準推薦。
mahout提供了一個介面，能把string轉為唯一的long型別資料，然後以map方式儲存起來，計算完成後再把long轉為string型別。

package test;  

import org.apache.mahout.cf.taste.impl.model.MemoryIDMigrator;  

public
 
 class TestMT {  

    public static void main(String[] args) {  
        // TODO Auto-generated method stub  
        String test = "d140615p10693zc";  
        MemoryIDMigrator thing2long = new MemoryIDMigrator();         
        Long testLong = thing2long.toLongID(test);        
        System.out.println(testLong);         
        thing2long.storeMapping(testLong, test);          
        String a =thing2long.toStringID(testLong);        
        System.out
 
.println(a);  
    }  
}  

MemoryIDMigrator 原始碼

package org.apache.mahout.cf.taste.impl.model;

import java.util.Iterator;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.model.AbstractIDMigrator;
import org.apache.mahout.cf.taste.model.UpdatableIDMigrator;

public
 
 final class MemoryIDMigrator extends AbstractIDMigrator implements UpdatableIDMigrator {
    private final FastByIDMap<String> longToString = new FastByIDMap(100);

    public MemoryIDMigrator() {
    }

    public void storeMapping(long longID, String stringID) {
        FastByIDMap var4 = this.longToString;
        synchronized(this.longToString) {
            this.longToString.put(longID, stringID);
        }
    }

    public String toStringID(long longID) {
        FastByIDMap var3 = this.longToString;
        synchronized(this.longToString) {
            return (String)this.longToString.get(longID);
        }
    }

    public void initialize(Iterable<String> stringIDs) {
        Iterator i$ = stringIDs.iterator();

        while(i$.hasNext()) {
            String stringID = (String)i$.next();
            this.storeMapping(this.toLongID(stringID), stringID);
        }

    }
}

下面是Facebook給出的Mahout協同過濾的測試程式碼

package com.xie;

/**
 * Created by liulu3 on 2017/1/18.
 */
import java.util.ArrayList;
import java.util.List;
import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
import org.apache.mahout.cf.taste.impl.model.GenericPreference;
import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray;
import org.apache.mahout.cf.taste.impl.model.MemoryIDMigrator;
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.recommender.svd.SVDPlusPlusFactorizer;
import org.apache.mahout.cf.taste.impl.recommender.svd.SVDRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.model.Preference;
import org.apache.mahout.cf.taste.model.PreferenceArray;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;

/**
 * This class shows some examples which are implemented
 * in java for showing how to use the different recommenders
 * in java.
 * It uses animals and foods as examples.
 * @author Manuel Blechschmidt <[email protected]>
 *
 */
public class AnimalFoodRecommender {

    private MemoryIDMigrator id2thing = new MemoryIDMigrator();
    private List<String> foods = new ArrayList<>();
    private List<String> animals = new ArrayList<>();
    private DataModel model;

    private Float[][] preferences = new Float[][]   {
            // Carrots  Grass  Pork Beef    Corn    Fish
            // Rabbit
            new Float[] {10f,  7f, 1f,  2f, null,  1f},
            // Cow
            new Float[] { 7f, 10f, null, null, null, null},
            // Dog
            new Float[] {null,  1f, 10f, 10f, null, null},
            // Pig
            new Float[] { 5f,  6f,  4f, null,  7f,  3f},
            // Chicken
            new Float[] { 7f,  6f,  2f, null, 10f, null},
            // Pinguin
            new Float[] { 2f,  2f, null,  2f,  2f, 10f},
            // Bear
            new Float[] { 2f, null,  8f,  8f,  2f,  7f},
            // Lion
            new Float[] {null, null,  9f, 10f,  2f, null},
            // Tiger
            new Float[] {null, null,  8f, null, null,  5f},
            // Antilope
            new Float[] { 6f, 10f,  1f,  1f, null, null},
            // Wolf
            new Float[] { 1f, null, null,  8f, null,  3f},
            // Sheep
            new Float[] {null,  8f, null, null, null, 2f}
    };


    public AnimalFoodRecommender() {
        initMemoryMigrator();
        initDataModel();
        initRecommender();
    }

    /**
     * This function generates ids for
     * the different things in the demp
     */
    private void initMemoryMigrator() {
        foods.add("Carrots");
        foods.add("Grass");
        foods.add("Pork");
        foods.add("Beef");
        foods.add("Corn");
        foods.add("Fish");
        for(String food : foods) {
            id2thing.storeMapping(id2thing.toLongID(food), food);
            System.out.println(food+" = "+id2thing.toLongID(food));
        }
        animals.add("Rabbit");
        animals.add("Cow");
        animals.add("Dog");
        animals.add("Pig");
        animals.add("Chicken");
        animals.add("Pinguin");
        animals.add("Bear");
        animals.add("Lion");
        animals.add("Tiger");
        animals.add("Antilope");
        animals.add("Wolf");
        animals.add("Sheep");
        for(String animal : animals) {
            id2thing.storeMapping(id2thing.toLongID(animal), animal);
            System.out.println(animal+" = "+id2thing.toLongID(animal));
        }
    }

    public void initDataModel() {
        FastByIDMap<PreferenceArray> preferenceMap = new FastByIDMap<>();
        for(int i=0;i<animals.size();i++) {
            List<Preference> userPreferences = new ArrayList<>();
            long userId = id2thing.toLongID(animals.get(i));
            for(int j=0;j<foods.size();j++) {
                if(preferences[i][j] != null) {
                    userPreferences.add(new GenericPreference(userId, id2thing.toLongID(foods.get(j)), preferences[i][j]));
                }
            }
            GenericUserPreferenceArray userArray = new GenericUserPreferenceArray(userPreferences);
            preferenceMap.put(userId, userArray);
        }
        model = new GenericDataModel(preferenceMap);
    }

    public void initRecommender() {
        try {

            PearsonCorrelationSimilarity pearsonSimilarity = new PearsonCorrelationSimilarity(model);

            // Java: Similarity between Wolf and Bear: 0.8196561646738477
            // R: corr(c(8,3,1),c(8,7,2)): 0.8196562
            System.out.println("Similarity between Wolf and Bear: "+pearsonSimilarity
                    .userSimilarity(id2thing.toLongID("Wolf"), id2thing.toLongID("Bear")));
            // Similarity between Wolf and Rabbit: -0.6465846072812313
            // R: cor(c(8,3,1),c(2,1,10)): -0.6465846
            System.out.println("Similarity between Wolf and Rabbit: "+pearsonSimilarity
                    .userSimilarity(id2thing.toLongID("Wolf"), id2thing.toLongID("Rabbit")));
            // Similarity between Wolf and Pinguin: -0.24019223070763077
            // R: cor(c(8,3,1),c(2,10,2)): -0.2401922
            System.out.println("Similarity between Wolf and Pinguin: "+pearsonSimilarity
                    .userSimilarity(id2thing.toLongID("Wolf"), id2thing.toLongID("Pinguin")));

            GenericUserBasedRecommender recommender = new GenericUserBasedRecommender(model,
                    new NearestNUserNeighborhood(3, pearsonSimilarity, model), pearsonSimilarity);
            for(RecommendedItem r : recommender.recommend(id2thing.toLongID("Wolf"), 3)) {
                System.out.println("UserBased: Wolf should eat: "+id2thing.toStringID(r.getItemID())+" Rating: "+r.getValue());
            }
            SVDRecommender svdrecommender = new SVDRecommender(model, new SVDPlusPlusFactorizer(model, 4, 1000));
            for(RecommendedItem r : svdrecommender.recommend(id2thing.toLongID("Sheep"), 3)) {
                System.out.println("SVD: Sheep should eat: "+id2thing.toStringID(r.getItemID())+" Rating: "+ r.getValue());
            }
        } catch (TasteException e) {
            e.printStackTrace();
        }
    }

    public static void main(String[] args) {
        new AnimalFoodRecommender();
    }
}