mahout笔记：基于用户的推荐例子

数据准备：

1,10,1.0
1,11,2.0
1,12,5.0
1,13,5.0
1,14,5.0
1,15,4.0
1,16,5.0
1,17,1.0
1,18,5.0
2,10,1.0
2,11,2.0
2,15,5.0
2,16,4.5
2,17,1.0
2,18,5.0
3,11,2.5
3,12,4.5
3,13,4.0
3,14,3.0
3,15,3.5
3,16,4.5
3,17,4.0
3,18,5.0
4,10,5.0
4,11,5.0
4,12,5.0
4,13,0.0
4,14,2.0
4,15,3.0
4,16,1.0
4,17,4.0
4,18,1.0

代码：

public class SampleRecommender {

	
	public static void main(String[] args) throws TasteException, IOException {
		
		DataModel model = new FileDataModel(new File("D:/data/datasets.csv"));
		UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
		UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
		UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
		
		List  recommendations = recommender.recommend(2, 3);
		for (RecommendedItem recommendation : recommendations) {
		  System.out.println(recommendation);
		}
	}

}

运行结果：

RecommendedItem[item:12, value:4.8328104]
RecommendedItem[item:13, value:4.6656213]
RecommendedItem[item:14, value:4.331242]

步骤：
1、建立数据模型

2、通过用户的item的品味计算用户相似性。

3、规定推荐系统使用相似性大于0.1

4、使用前面对象创建推荐系统

5、测试推荐系统结果

推荐系统评估：

public class EvaluateRecommender {

	public static class MyRecommenderBuilder implements RecommenderBuilder {
		
		@Override
		public Recommender buildRecommender(DataModel dataModel)
				throws TasteException {
			UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
			UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, dataModel);
			return new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
			
		}
		
	}
	
	public static void main(String[] args) throws IOException, TasteException {
		DataModel model = new FileDataModel(new File("D:/data/datasets.csv"));
		RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
		RecommenderBuilder builder = new MyRecommenderBuilder();
		double result = evaluator.evaluate(builder, null, model, 0.9, 1.0);
		System.out.println(result);
		
		
	}

}