mahout 入门

数据准备

intro.csv

1,101,5.0
1,102,3.0
1,103,2.5

2,101,2.0
2,102,2.5
2,103,5.0
2,104,2.0

3,101,2.5
3,104,4.0
3,105,4.5
3,107,5.0

4,101,5.0
4,103,3.0
4,104,4.5
4,106,4.0

5,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5
5,106,4.0

 

测试代码

下面是基于用户维度的推荐

        DataModel model = new FileDataModel(new File("/home/yunpeng/test2/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);
        }
        

 

下面是比较两份内容之间的关联

		DataModel model = new FileDataModel(new File("/home/yunpeng/test2/intro.csv"));
		ItemSimilarity similarity = new PearsonCorrelationSimilarity(model);
		ItemBasedRecommender recommender = new GenericItemBasedRecommender(model, similarity);
		List<RecommendedItem> recommendations = recommender.mostSimilarItems(101, 3);
		for (RecommendedItem recommendation : recommendations) {
			System.out.println(recommendation);
		}