mahout 为约会数据集推荐

在http://libimseti.cz中下载约会的数据集

里面的ratings.dat有257MB，以逗号分隔，包含用户ID,档案ID和评分（档案ID和用户ID不是采用同一个匿名方法）

这个数据集经过了预处理，剔除了生成评分个数不到20个的用户，也剔除了对每个档案都给出相同分值的用户

根据《mahout 实战》中所说，最优的配置是基于用户的推荐，采用欧氏距离，近邻数量为2.

由此写出的评估程序如下：

public static void evaluateDateData() throws IOException, TasteException

{

DataModel model = new FileDataModel(new File("F:\\mahout\\libimseti\\libimseti-complete\\libimseti\\ratings.dat"));

RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();

RecommenderBuilder builder = new RecommenderBuilder(){

@Override

public Recommender buildRecommender(DataModel model)

throws TasteException {

// TODO Auto-generated method stub

UserSimilarity similarity = new EuclideanDistanceSimilarity(model);

UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);

return new GenericUserBasedRecommender(model, neighborhood, similarity);

}

};

double score = evaluator.evaluate(builder, null, model, 0.95, 0.05);

System.out.println(score);

}

运行结果为0.8415841584158418

在这里的参数设为-Xmx1024m