這里的代碼屬于mahout in action中的內(nèi)容，只是我不了解其中的注解，所以就特意想學(xué)習(xí)一下相關(guān)的內(nèi)容。

package mia.recommender.ch02;//=分析導(dǎo)入包可以看出mahout的包分為主要類以及它們的實(shí)現(xiàn)類=import org.apache.mahout.cf.taste.impl.model.file.*;import org.apache.mahout.cf.taste.impl.neighborhood.*;import org.apache.mahout.cf.taste.impl.recommender.*;import org.apache.mahout.cf.taste.impl.similarity.*;import org.apache.mahout.cf.taste.model.*;import org.apache.mahout.cf.taste.neighborhood.*;import org.apache.mahout.cf.taste.recommender.*;import org.apache.mahout.cf.taste.similarity.*;import java.io.*;import java.util.*;class RecommenderIntro {  public static void main(String[] args) throws Exception { //=如何從csv的文件中構(gòu)建mahout的數(shù)據(jù)表示，DataModel就是來表示<user，item，rating>的知識(shí)的=    DataModel model = new FileDataModel(new File("intro.csv"));//=user-based的第一步就是找相似用戶，所以要定義用戶的相似性，包括用什么相似性度量，以及鄰居的參數(shù)=    UserSimilarity similarity = new PearsonCorrelationSimilarity(model);    UserNeighborhood neighborhood =  new NearestNUserNeighborhood(2, similarity, model);//=一旦確定了相鄰用戶，那么一個(gè)普通的user-based推薦器就可以被構(gòu)建起來了=    Recommender recommender = new GenericUserBasedRecommender(        model, neighborhood, similarity);//=我們可以來使用它，這里是向用戶1推薦1個(gè)商品=    List<RecommendedItem> recommendations =        recommender.recommend(1, 1);//=推薦的結(jié)果可以輸出，這里是：RecommendedItem[item:104, value:4.257081]=    for (RecommendedItem recommendation : recommendations) {      System.out.PRintln(recommendation);    }  }}復(fù)制代碼復(fù)制代碼二， Evaluation復(fù)制代碼復(fù)制代碼package mia.recommender.ch02;import org.apache.mahout.cf.taste.impl.model.file.*;import org.apache.mahout.cf.taste.impl.neighborhood.*;import org.apache.mahout.cf.taste.impl.recommender.*;import org.apache.mahout.cf.taste.impl.similarity.*;import org.apache.mahout.cf.taste.model.*;import org.apache.mahout.cf.taste.neighborhood.*;import org.apache.mahout.cf.taste.recommender.*;import org.apache.mahout.cf.taste.similarity.*;import java.io.*;import java.util.*;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.eval.RecommenderBuilder;import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;import org.apache.mahout.common.RandomUtils;/** * * @author wentingtu <wentingtu09 at Gmail dot com> */public class RecommenderEvalu{    public static void main(String[] args) throws IOException, TasteException    {        //=導(dǎo)入org.apache.mahout.common.RandomUtils;=        //這個(gè)是產(chǎn)生唯一的種子使得在劃分訓(xùn)練和測(cè)試數(shù)據(jù)的時(shí)候具有唯一性=         RandomUtils.useTestSeed();               DataModel model = new FileDataModel(new File("intro.csv"));        //構(gòu)建評(píng)估器，這里用到的性能度量是每個(gè)sum( |預(yù)測(cè)值 - 真實(shí)值| ) / 值的個(gè)數(shù)        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();        //=導(dǎo)入 org.apache.mahout.cf.taste.eval.RecommenderBuilder;=        //這里要涉及用到了一個(gè)定義推薦器構(gòu)造方法的類：RecommenderBuilder        RecommenderBuilder builder = new RecommenderBuilder()        {        //使用方法是重載buildRecommender函數(shù)，函數(shù)里是構(gòu)造推薦器的方法            @Override            public Recommender buildRecommender(DataModel model)                    throws TasteException            {                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);                UserNeighborhood neighborhood =                        new NearestNUserNeighborhood(2, similarity, model);                return new GenericUserBasedRecommender(model, neighborhood, similarity);            }        };    //=導(dǎo)入 org.apache.mahout.cf.taste.eval.RecommenderEvaluator;=   //調(diào)用評(píng)估器，輸入有上面構(gòu)造的推薦器方法，數(shù)據(jù)模型，訓(xùn)練/全部 比例，驗(yàn)證數(shù)據(jù)/數(shù)據(jù) 比例       double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);   //輸出評(píng)價(jià)結(jié)果：1.0 證明最后的估計(jì)結(jié)果是  AverageAbsoluteDifference = 1.0     System.out.println(score);    }}復(fù)制代碼復(fù)制代碼復(fù)制代碼復(fù)制代碼package mia.recommender.ch02;import org.apache.mahout.cf.taste.impl.model.file.*;import org.apache.mahout.cf.taste.impl.neighborhood.*;import org.apache.mahout.cf.taste.impl.recommender.*;import org.apache.mahout.cf.taste.impl.similarity.*;import org.apache.mahout.cf.taste.model.*;import org.apache.mahout.cf.taste.neighborhood.*;import org.apache.mahout.cf.taste.recommender.*;import org.apache.mahout.cf.taste.similarity.*;import java.io.*;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.eval.IRStatistics;import org.apache.mahout.cf.taste.eval.RecommenderBuilder;import org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;import org.apache.mahout.cf.taste.impl.eval.GenericRecommenderIRStatsEvaluator;import org.apache.mahout.common.RandomUtils;/** * * @author Administrator */public class RecommenderEvaluPrecisionRecall {    public static void main(String[] args) throws IOException, TasteException {        RandomUtils.useTestSeed();        DataModel model = new FileDataModel(new File("intro.csv"));        //=導(dǎo)入org.apache.mahout.cf.taste.eval.RecommenderIRStatsEvaluator;=        //構(gòu)建評(píng)估器        RecommenderIRStatsEvaluator evaluator =                new GenericRecommenderIRStatsEvaluator();                RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {            @Override            public Recommender buildRecommender(DataModel model)                    throws TasteException {                UserSimilarity similarity = new PearsonCorrelationSimilarity(model);                UserNeighborhood neighborhood =                        new NearestNUserNeighborhood(2, similarity, model);                return new GenericUserBasedRecommender(model, neighborhood, similarity);            }        };        //使用評(píng)估器，并設(shè)定評(píng)估期的參數(shù)        //2表示"precision and recall at 2"即相當(dāng)于推薦top2，然后在top-2的推薦上計(jì)算準(zhǔn)確率和召回率        //既然涉及到準(zhǔn)確率和召回率，這里就有一個(gè)"hit"的定義，就是怎樣的一個(gè)推薦算是good        //下面的參數(shù)設(shè)置是這樣定義"good"的：利用閾值threshold = μ + σ         //即 user's average preference value μ plus one standard deviation σ        //如果一個(gè)推薦，它的真實(shí)分值是高于threshold的，那么它就是"good"        IRStatistics stats = evaluator.evaluate(                recommenderBuilder, null, model, null, 2,                GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,                1.0);        //輸出為0.75 1.0        System.out.println(stats.getPrecision());        System.out.println(stats.getRecall());    }}復(fù)制代碼復(fù)制代碼三，Set preference復(fù)制代碼復(fù)制代碼package mia.recommender.ch03;import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray;import org.apache.mahout.cf.taste.model.Preference;import org.apache.mahout.cf.taste.model.PreferenceArray;/** * * @author Administrator */public class SetPrefinPreferenceArray {    /**     * @param args the command line arguments     */    public static void main(String[] args) {        PreferenceArray user1Prefs = new GenericUserPreferenceArray(2);        user1Prefs.setUserID(0, 1L);        user1Prefs.setItemID(0, 101L);        user1Prefs.setValue(0, 2.0f);        user1Prefs.setItemID(1, 102L);        user1Prefs.setValue(1, 3.0f);        Preference pref = user1Prefs.get(1);    }}復(fù)制代碼復(fù)制代碼四，User-based CF復(fù)制代碼復(fù)制代碼package mia.recommender.ch05;import java.io.File;import java.io.IOException;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.eval.RecommenderBuilder;import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;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.neighborhood.ThresholdUserNeighborhood;import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity;import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;import org.apache.mahout.cf.taste.recommender.Recommender;import org.apache.mahout.cf.taste.similarity.UserSimilarity;import org.apache.mahout.common.RandomUtils;/** * * @author Administrator */public class UserBasedCF {    public static void recommenderModelEvaluation(DataModel model) throws TasteException {        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();        RandomUtils.useTestSeed();        RecommenderBuilder builder = new RecommenderBuilder() {            //=============實(shí)驗(yàn)參數(shù)設(shè)置===============            //1.K近鄰 or 閾值近鄰            //近鄰：K？            //閾值近鄰：threshold？            //2.相似度量：Euclidean ， Pearson ， Log-likelihood ， Tanimoto             char similarityPattern = 'E';//'E' or 'P' or 'L' or 'T'            char neighborhoodPattern = 'K';//'K' or 'T'            int k = 2;            double threshold = 0.5;            @Override            public Recommender buildRecommender(DataModel dm) throws TasteException {                UserSimilarity similarity = null;                UserNeighborhood neighborhood = null;                switch (similarityPattern) {                    case 'E': {                        similarity = new EuclideanDistanceSimilarity(dm);                    }                    case 'P': {                        similarity = new PearsonCorrelationSimilarity(dm);                    }                    case 'L': {                        similarity = new LogLikelihoodSimilarity(dm);                    }                    case 'T': {                        similarity = new TanimotoCoefficientSimilarity(dm);                    }                }                switch (neighborhoodPattern) {                    case 'K': {                        neighborhood = new NearestNUserNeighborhood(k, similarity, dm);                    }                    case 'T': {                        neighborhood = new ThresholdUserNeighborhood(threshold, similarity, dm);                    }                }                return new GenericUserBasedRecommender(dm, neighborhood, similarity);            }        };        double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);        System.out.println(score);    }    /**     * @param args the command line arguments     */    public static void main(String[] args) throws IOException, TasteException {        DataModel model = new FileDataModel(new File("data/dating/ratings.dat"));        recommenderModelEvaluation(model);    }}復(fù)制代碼復(fù)制代碼五，Item-based CF復(fù)制代碼復(fù)制代碼package mia.recommender.ch05;import java.io.File;import java.io.IOException;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.eval.RecommenderBuilder;import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;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.EuclideanDistanceSimilarity;import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity;import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.recommender.Recommender;import org.apache.mahout.cf.taste.similarity.ItemSimilarity;import org.apache.mahout.common.RandomUtils;/** * * @author Administrator */public class ItemBasedCF {   public static void recommenderModelEvaluation(DataModel model) throws TasteException {        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();        RandomUtils.useTestSeed();        RecommenderBuilder builder = new RecommenderBuilder() {            //=============實(shí)驗(yàn)參數(shù)設(shè)置===============            //1.K近鄰 or 閾值近鄰            //近鄰：K？            //閾值近鄰：threshold？            //2.相似度量：Euclidean ， Pearson ， Log-likelihood ， Tanimoto             char similarityPattern = 'E';//'E' or 'P' or 'L' or 'T'            @Override            public Recommender buildRecommender(DataModel dm) throws TasteException {                ItemSimilarity similarity = null;                switch (similarityPattern) {                    case 'E': {                        similarity = new EuclideanDistanceSimilarity(dm);                    }                    case 'P': {                        similarity = new PearsonCorrelationSimilarity(dm);                    }                    case 'L': {                        similarity = new LogLikelihoodSimilarity(dm);                    }                    case 'T': {                        similarity = new TanimotoCoefficientSimilarity(dm);                    }                }                return new GenericItemBasedRecommender(dm, similarity);            }        };        double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);        System.out.println(score);    }    /**     * @param args the command line arguments     */    public static void main(String[] args) throws IOException, TasteException {        DataModel model = new FileDataModel(new File("data/dating/ratings.dat"));        recommenderModelEvaluation(model);    }}復(fù)制代碼復(fù)制代碼六，Slope one CF復(fù)制代碼復(fù)制代碼package mia.recommender.ch05;import java.io.File;import java.io.IOException;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.common.Weighting;import org.apache.mahout.cf.taste.eval.RecommenderBuilder;import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;import org.apache.mahout.cf.taste.impl.recommender.slopeone.MemoryDiffStorage;import org.apache.mahout.cf.taste.impl.recommender.slopeone.SlopeOneRecommender;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.recommender.Recommender;import org.apache.mahout.cf.taste.recommender.slopeone.DiffStorage;import org.apache.mahout.common.RandomUtils;/** * * @author Administrator */public class SlopeOneCF {    public static void recommenderModelEvaluation(DataModel model) throws TasteException {        RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();        RandomUtils.useTestSeed();        RecommenderBuilder builder = new RecommenderBuilder() {            long diffStorageNb = 100000;            @Override            public Recommender buildRecommender(DataModel dm) throws TasteException {                DiffStorage diffStorage = new MemoryDiffStorage(dm, Weighting.WEIGHTED, diffStorageNb);                return new SlopeOneRecommender(dm, Weighting.WEIGHTED, Weighting.WEIGHTED, diffStorage);            }        };    }    /**     * @param args the command line arguments     */    public static void main(String[] args) throws IOException, TasteException {        DataModel model = new FileDataModel(new File("data/dating/ratings.dat"));        recommenderModelEvaluation(model);    }}復(fù)制代碼復(fù)制代碼七，一個(gè)示例復(fù)制代碼復(fù)制代碼package mia.recommender.ch05;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.GenericUserPreferenceArray;import org.apache.mahout.cf.taste.impl.model.PlusAnonymousUserDataModel;import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.model.PreferenceArray;import org.apache.mahout.cf.taste.recommender.RecommendedItem;/** * * @author Administrator */public class LibimsetiWithAnonymousRecommender extends LibimsetiRecommender {    private final PlusAnonymousUserDataModel plusAnonymousModel;    public LibimsetiWithAnonymousRecommender()            throws TasteException, IOException {        this((DataModel) new FileDataModel(new File("data/dating/ratings.dat")));    }    public LibimsetiWithAnonymousRecommender(DataModel model)            throws TasteException, IOException {        //調(diào)用父類LibimsetiRecommender的構(gòu)造函數(shù)        super(new PlusAnonymousUserDataModel(model));        //得到PlusAnonymousUserDataModel對(duì)象        plusAnonymousModel =                (PlusAnonymousUserDataModel) getDataModel();    }    //設(shè)計(jì)這個(gè)推薦器的recommend方法：輸入：匿名用戶的評(píng)分信息 輸出：對(duì)此匿名用戶的推薦    public synchronized List<RecommendedItem> recommend(            PreferenceArray anonymousUserPrefs, int topN)            throws TasteException {        //利用PlusAnonymousUserDataModel對(duì)象的setTempPrefs方法為將匿名用戶加入到數(shù)據(jù)中，        //并且利用PlusAnonymousUserDataModel.TEMP_USER_ID作為其userID        plusAnonymousModel.setTempPrefs(anonymousUserPrefs);        //調(diào)用父類LibimsetiRecommender的recommend方法        //userID現(xiàn)在被PlusAnonymousUserDataModel.TEMP_USER_ID所代替了        List<RecommendedItem> recommendations =                recommend(PlusAnonymousUserDataModel.TEMP_USER_ID, topN, null);        //刪除PlusAnonymousUserDataModel.TEMP_USER_ID與匿名用戶的關(guān)聯(lián)        plusAnonymousModel.clearTempPrefs();        return recommendations;    }    //創(chuàng)建當(dāng)前匿名用戶的偽數(shù)據(jù)    public PreferenceArray creatAnAnonymousPrefs() {        PreferenceArray anonymousPrefs =                new GenericUserPreferenceArray(3);        anonymousPrefs.setUserID(0, PlusAnonymousUserDataModel.TEMP_USER_ID);        anonymousPrefs.setItemID(0, 123L);        anonymousPrefs.setValue(0, 1.0f);        anonymousPrefs.setItemID(1, 123L);        anonymousPrefs.setValue(1, 3.0f);        anonymousPrefs.setItemID(2, 123L);        anonymousPrefs.setValue(2, 2.0f);        return anonymousPrefs;    }    public static void main(String[] args) throws Exception {        LibimsetiWithAnonymousRecommender recommender =                new LibimsetiWithAnonymousRecommender();        List<RecommendedItem> recommendations =                recommender.recommend(recommender.creatAnAnonymousPrefs(), 10);        System.out.println(recommendations);    }}復(fù)制代碼復(fù)制代碼復(fù)制代碼復(fù)制代碼package mia.recommender.ch05;import java.io.File;import java.io.IOException;import java.util.Collection;import java.util.List;import org.apache.mahout.cf.taste.common.Refreshable;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.impl.common.FastIDSet;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.EuclideanDistanceSimilarity;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;import org.apache.mahout.cf.taste.recommender.IDRescorer;import org.apache.mahout.cf.taste.recommender.RecommendedItem;import org.apache.mahout.cf.taste.recommender.Recommender;import org.apache.mahout.cf.taste.similarity.UserSimilarity;/** * * @author Administrator */public class LibimsetiRecommender implements Recommender {    private final Recommender libimsetiRecommender;    private final DataModel model;    private final FastIDSet men;    private final FastIDSet women;    //構(gòu)造函數(shù)：一般而言，一個(gè)普適的自定義推薦器的輸入應(yīng)該是：DataModel和額外的知識(shí)    //應(yīng)該將獨(dú)立于數(shù)據(jù)的東西構(gòu)建好：基本的pure CF推薦器    public LibimsetiRecommender() throws TasteException, IOException {        this((DataModel) new FileDataModel(new File("data/dating/ratings.dat")));    }   //應(yīng)該將獨(dú)立于數(shù)據(jù)的東西構(gòu)建好：基本的pure CF推薦器,即將libimsetiRecommender設(shè)為pure CF    public LibimsetiRecommender(DataModel model) throws TasteException, IOException {        UserSimilarity similarity = new EuclideanDistanceSimilarity(model);        UserNeighborhood neighborhood =                new NearestNUserNeighborhood(2, similarity, model);        libimsetiRecommender = new GenericUserBasedRecommender(model, neighborhood, similarity);        this.model = model;        FastIDSet[] menWomen = GenderRescorer.generateMenWomen(                new File(("gender.dat")));        men = menWomen[0];        women = menWomen[1];    }   //用libimsetiRecommender進(jìn)行推薦時(shí)就加入了由gender信息定義的GenderRescorer    public List<RecommendedItem> recommend(long userID, int topN) throws TasteException {        IDRescorer rescorer = new GenderRescorer(men, women, userID, model);        return libimsetiRecommender.recommend(userID, topN, rescorer);             }   //用libimsetiRecommender也提供了自定義IDRescorer進(jìn)行推薦的方法    public List<RecommendedItem> recommend(long userID, int topN, IDRescorer idr) throws TasteException {       return libimsetiRecommender.recommend(userID, topN, idr);     }   //這里要注意，由于libimsetiRecommender真正進(jìn)行preference的估計(jì)是要受到GenderRescorer的rescore的影響的    public float estimatePreference(long userID, long itemID) throws TasteException {         IDRescorer rescorer = new GenderRescorer(men, women, userID, model);     return (float) rescorer.rescore(         itemID, libimsetiRecommender.estimatePreference(userID, itemID));    }   //這個(gè)可以直接借助于libimsetiRecommender的setPreference    public void setPreference(long userID, long itemID, float value) throws TasteException {        libimsetiRecommender.setPreference(userID, itemID, value);    }   //這個(gè)可以直接借助于libimsetiRecommender的removePreference    public void removePreference(long userID, long itemID) throws TasteException {        libimsetiRecommender.removePreference(userID, itemID);    }   //這個(gè)可以直接借助于libimsetiRecommender的getDataModel    public DataModel getDataModel() {        return libimsetiRecommender.getDataModel();     }   //這個(gè)可以直接借助于libimsetiRecommender的refresh    public void refresh(Collection<Refreshable> alreadyRefreshed) {        libimsetiRecommender.refresh(alreadyRefreshed);    }}復(fù)制代碼復(fù)制代碼復(fù)制代碼復(fù)制代碼package mia.recommender.ch05;import java.io.File;import java.io.IOException;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.impl.common.FastIDSet;import org.apache.mahout.cf.taste.model.DataModel;import org.apache.mahout.cf.taste.model.PreferenceArray;import org.apache.mahout.cf.taste.recommender.IDRescorer;import org.apache.mahout.common.iterator.FileLineIterable;/** * * @author Administrator */public class GenderRescorer implements IDRescorer {    private final FastIDSet men;//存放當(dāng)前數(shù)據(jù)模型對(duì)應(yīng)的所有male selectableUser    private final FastIDSet women;//存放當(dāng)前數(shù)據(jù)模型對(duì)應(yīng)的所有female selectableUser    private final FastIDSet usersRateMoreMen;//    private final FastIDSet usersRateLessMen;    private final boolean likeMen;//表明針對(duì)一個(gè)用戶（userID定義）一個(gè)profileID是否應(yīng)該過濾    public GenderRescorer(            FastIDSet men,            FastIDSet women,            long userID, DataModel model)            throws TasteException {        this.men = men;        this.women = women;        this.usersRateMoreMen = new FastIDSet();        this.usersRateLessMen = new FastIDSet();        this.likeMen = ratesMoreMen(userID, model);    }    //產(chǎn)生數(shù)據(jù)對(duì)應(yīng)的men和women集合    public static FastIDSet[] generateMenWomen(File genderFile)            throws IOException {        FastIDSet men = new FastIDSet(50000);        FastIDSet women = new FastIDSet(50000);        for (String line : new FileLineIterable(genderFile)) {            int comma = line.indexOf(',');            char gender = line.charAt(comma + 1);            if (gender == 'U') {                continue;            }            long profileID = Long.parseLong(line.substring(0, comma));            if (gender == 'M') {                men.add(profileID);            } else {                women.add(profileID);            }        }        men.rehash();        women.rehash();        return new FastIDSet[]{men, women};    }    //判斷userID對(duì)應(yīng)的用戶是不是更喜歡男性，從他/她評(píng)過分的那些用戶的性別來統(tǒng)計(jì)    private boolean ratesMoreMen(long userID, DataModel model)            throws TasteException {        if (usersRateMoreMen.contains(userID)) {            return true;        }        if (usersRateLessMen.contains(userID)) {            return false;        }        PreferenceArray prefs = model.getPreferencesFromUser(userID);        int menCount = 0;        int womenCount = 0;        for (int i = 0; i < prefs.length(); i++) {            long profileID = prefs.get(i).getItemID();            if (men.contains(profileID)) {                menCount++;            } else if (women.contains(profileID)) {                womenCount++;            }        }        boolean ratesMoreMen = menCount > womenCount;        if (ratesMoreMen) {            usersRateMoreMen.add(userID);        } else {            usersRateLessMen.add(userID);        }        return ratesMoreMen;    }   //對(duì)于需要過濾的推薦，設(shè)置其值為NaN，這是因?yàn)樗麄儾皇遣荒芡扑]的，而是最差的推薦    public double rescore(long profileID, double originalScore) {        return isFiltered(profileID) ? Double.NaN : originalScore;    }    //如果一個(gè)用戶是喜歡男性的，而推薦的又是女性，則這個(gè)推薦是應(yīng)該過濾掉的，反之亦然    public boolean isFiltered(long profileID) {        return likeMen ? women.contains(profileID) : men.contains(profileID);    }}復(fù)制代碼復(fù)制代碼復(fù)制代碼復(fù)制代碼package mia.recommender.ch05;import java.util.Collection;import org.apache.mahout.cf.taste.common.Refreshable;import org.apache.mahout.cf.taste.common.TasteException;import org.apache.mahout.cf.taste.impl.common.FastIDSet;import org.apache.mahout.cf.taste.similarity.ItemSimilarity;/** * * @author Administrator */public class GenderItemSimilarity  implements ItemSimilarity  {  private final FastIDSet men;   private final FastIDSet women;    public GenderItemSimilarity(FastIDSet men, FastIDSet women) {     this.men = men;     this.women = women;   }      public double itemSimilarity(long profileID1, long profileID2) throws TasteException {    Boolean profile1IsMan = isMan(profileID1);     if (profile1IsMan == null) {       return 0.0;     }     Boolean profile2IsMan = isMan(profileID2);     if (profile2IsMan == null) {       return 0.0;     }     return profile1IsMan == profile2IsMan ? 1.0 : -1.0;     }        private Boolean isMan(long profileID) {     if (men.contains(profileID)) {       return Boolean.TRUE;     }     if (women.contains(profileID)) {       return Boolean.FALSE;     }     return null;   }     public double[] itemSimilarities(long itemID1, long[] itemID2s) throws TasteException{    double[] result = new double[itemID2s.length];     for (int i = 0; i < itemID2s.length; i++) {       result[i] = itemSimilarity(itemID1, itemID2s[i]);     }     return result;   }      public long[] allSimilarItemIDs(long l) throws TasteException {        throw new UnsupportedOperationException("Not supported yet.");    }    public void refresh(Collection<Refreshable> clctn) {        throw new UnsupportedOperationException("Not supported yet.");    }    }頂0