Mahout推荐引擎使用

1基于用户的推荐引擎

Datamodel：提供存储和访问用户、项和偏好数据，用于计算

Usersimilarity：计算用户之间的相似度

UserNeighborhood：计算用户的邻居

Recommender ：将上述组件组织在一起，为用户提供item推荐

package com.taobao.afan;

import java.io.File;

import java.util.List;

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.model.DataModel;

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 RecommenderIntro {

public static void main(String[] args) throws Exception{

// TODO Auto-generated method stub

DataModel model = new FileDataModel(new File("./intro.csv")); //加载数据文件

UserSimilarity similarity = new PearsonCorrelationSimilarity(model); //构建相似度计算方式

UserNeighborhood neighborhood =

new NearestNUserNeighborhood(2, similarity, model);

Recommender recommender = new GenericUserBasedRecommender(

model, neighborhood, similarity); //创建推荐引擎

List<RecommendedItem> recommendations =

recommender.recommend(1, 1); //给用户1推荐一个item

for (RecommendedItem recommendation : recommendations) {

System.out.println(recommendation);

}

}

}

输出：

2011-2-8 12:42:46 org.slf4j.impl.JCLLoggerAdapter info

信息: Creating FileDataModel for file ./intro.csv

2011-2-8 12:42:48 org.slf4j.impl.JCLLoggerAdapter info

信息: Reading file info...

2011-2-8 12:42:48 org.slf4j.impl.JCLLoggerAdapter info

信息: Read lines: 21

2011-2-8 12:42:48 org.slf4j.impl.JCLLoggerAdapter info

信息: Processed 5 users

RecommendedItem[item:104, value:4.253491]

推荐引擎评价

package com.taobao.afan;

import java.io.File;

import org.apache.mahout.cf.taste.common.TasteException;

import org.apache.mahout.cf.taste.eval.RecommenderBuilder;

import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;

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;

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.eval.RecommenderEvaluator;

public class EvaluatorInfo {

public static void main(String[] args) throws Exception{

// TODO Auto-generated method stub

RandomUtils.useTestSeed();

DataModel model = new FileDataModel(new File("./intro.csv"));

RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();

RecommenderBuilder builder = 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);

}

};

double score = evaluator.evaluate(builder, null, model, 0.7, 1.0);

System.out.println(score);

}

}

输出：

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Creating FileDataModel for file ./intro.csv

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Beginning evaluation using 0.7 of FileDataModel[dataFile:/root/workspace/RecommenderIntro/./intro.csv]

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Reading file info...

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Read lines: 21

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Processed 5 users

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Processed 5 users

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Beginning evaluation of 3 users

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Starting timing of 3 tasks in 1 threads

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Average time per recommendation: 11ms

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Approximate memory used: 1MB / 7MB

2011-2-8 13:35:31 org.slf4j.impl.JCLLoggerAdapter info

信息: Evaluation result: 0.5

0.5

2评价准确性和返回率

package com.taobao.afan;

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.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.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 java.io.File;

class IREvaluatorIntro {

public static void main(String[] args) throws Exception {

DataModel model = new FileDataModel(new File("./intro.csv"));

RecommenderIRStatsEvaluator evaluator =

new GenericRecommenderIRStatsEvaluator();

// 创建推荐引擎

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);

}

};

// 评估准确性和返回率 "at 2":

IRStatistics stats = evaluator.evaluate(recommenderBuilder,

null, model, null, 2,

GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,

1.0);

System.out.println(stats.getPrecision());

System.out.println(stats.getRecall());

}

}

输出：

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Creating FileDataModel for file ./intro.csv

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Reading file info...

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Read lines: 21

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Processed 5 users

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Processed 5 users

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Evaluated with user 2 in 29ms

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Precision/recall/fall-out: 0.0 / 0.0 / 0.3333333333333333

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Processed 5 users

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Evaluated with user 4 in 0ms

2011-2-8 13:44:52 org.slf4j.impl.JCLLoggerAdapter info

信息: Precision/recall/fall-out: 0.25 / 0.5 / 0.25

0.25

0.5