上篇文章介绍了协同过滤的安装与配置，这篇找了几个协同过滤的简单例子，看一下
Mahout给我们提供的强大的协同过滤算法。需要新建一个基于Maven的工程，下面是
pom.xml需要导入的包。
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<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" <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> |
这里我们导入的是最新的Mahout包，需要在本地的maven库中安装好。
首先我们需要准备好测试的数据，我们就用《Mahout in action》中的例子：
1,101,51,102,31,103,2.52,101,22,102,2.52,103,52,104,23,101,2.53,104,43,105,4.53,107,54,101,54,103,34,104,4.54,106,45,101,45,102,35,103,25,104,45,105,3.55,106,4 |
具体对应的关系图如下：
下面我们用Mahout中三种不同的推荐代码来执行以下刚才给出的数据，看看Mahout中的推荐接口是
如何使用的。
1. 基于用户的协同推荐的代码：
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DataModel model =newFileDataModel(newFile("data/intro.csv"));UserSimilarity similarity =newPearsonCorrelationSimilarity(model);UserNeighborhood neighborhood =newNearestNUserNeighborhood(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);} |
执行后的结果是：RecommendedItem[item:104, value:4.257081]
2. 基于Item的协同过滤的代码：
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DataModel model =newFileDataModel(newFile("data/intro.csv"));ItemSimilarity similarity =newPearsonCorrelationSimilarity(model);Recommender recommender=new GenericItemBasedRecommender(model,similarity);List<RecommendedItem> recommendations =recommender.recommend(1,1);for(RecommendedItem recommendation :recommendations){ System.out.println(recommendation);} |
执行后的结果是：RecommendedItem[item:104, value:5.0]
3. SlopeOne推荐算法
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DataModel model =newFileDataModel(newFile("data/intro.csv"));Recommender recommender=new SlopeOneRecommender(model);List<RecommendedItem> recommendations =recommender.recommend(1,1);for(RecommendedItem recommendation :recommendations){ System.out.println(recommendation);} |
执行结果是：RecommendedItem[item:105, value:5.75]
http://www.cnblogs.com/cstar/archive/2012/12/17/2821316.html