Mahout協同推薦簡單實例

    上篇文章介紹了協同過濾的安裝與配置，這篇找了幾個協同過濾的簡單例子，看一下

Mahout給我們提供的強大的協同過濾算法。需要新建一個基於Maven的工程，下面是

pom.xml需要導入的包。

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>mahouttest</groupId>
  <artifactId>mahouttest</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <packaging>jar</packaging>
  <name>mahouttest</name>
  <url>http://maven.apache.org</url>
  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>
  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.8.1</version>
      <scope>test</scope>
    </dependency>
    <dependency>
    	<groupId>org.apache.mahout</groupId>
    	<artifactId>mahout-core</artifactId>
    	<version>0.8-SNAPSHOT</version>
    	<type>jar</type>
    	<scope>compile</scope>
    </dependency>
  </dependencies>

　　這里我們導入的是最新的Mahout包，需要在本地的maven庫中安裝好。

首先我們需要准備好測試的數據，我們就用《Mahout  in action》中的例子：

1,101,5
1,102,3
1,103,2.5
2,101,2
2,102,2.5
2,103,5
2,104,2
3,101,2.5
3,104,4
3,105,4.5
3,107,5
4,101,5
4,103,3
4,104,4.5
4,106,4
5,101,4
5,102,3
5,103,2
5,104,4
5,105,3.5
5,106,4

具體對應的關系圖如下：

下面我們用Mahout中三種不同的推薦代碼來執行以下剛才給出的數據，看看Mahout中的推薦接口是

如何使用的。

1. 基於用戶的協同推薦的代碼：

DataModel  model =new FileDataModel(new File("data/intro.csv"));
UserSimilarity similarity =new PearsonCorrelationSimilarity(model);
UserNeighborhood neighborhood =new NearestNUserNeighborhood(2,similarity,model);
Recommender recommender= new GenericUserBasedRecommender(model,neighborhood,similarity);
List<RecommendedItem> recommendations =recommender.recommend(1, 1);
for(RecommendedItem recommendation :recommendations){
	System.out.println(recommendation);
}

執行后的結果是：RecommendedItem[item:104, value:4.257081]

2. 基於Item的協同過濾的代碼：

DataModel  model =new FileDataModel(new File("data/intro.csv"));
ItemSimilarity similarity =new PearsonCorrelationSimilarity(model);
Recommender recommender= new GenericItemBasedRecommender(model,similarity);
List<RecommendedItem> recommendations =recommender.recommend(1, 1);
for(RecommendedItem recommendation :recommendations){
    System.out.println(recommendation);
}

執行后的結果是：RecommendedItem[item:104, value:5.0]

3. SlopeOne推薦算法

DataModel  model =new FileDataModel(new File("data/intro.csv"));
Recommender recommender= new SlopeOneRecommender(model);
List<RecommendedItem> recommendations =recommender.recommend(1, 1);
for(RecommendedItem recommendation :recommendations){
    System.out.println(recommendation);
}

執行結果是：RecommendedItem[item:105, value:5.75]