Mahout之Item-based应用使用

环境：

<dependency>
	<groupId>org.apache.mahout</groupId>
	<artifactId>mahout-core</artifactId>
	<version>0.8</version>
</dependency>
<dependency>
	<groupId>org.apache.mahout</groupId>
	<artifactId>mahout-math</artifactId>
	<version>0.8</version>
</dependency>
<dependency>
	<groupId>org.apache.mahout</groupId>
	<artifactId>mahout-integration</artifactId>
	<version>0.8</version>
</dependency>

概述：

基于item的相似，首先基于用户评分矩阵构建item之间的相似矩阵，从而对用户感兴趣的item做相关tiem的推荐。

应用：

private static ItemSimilarity getISInstance(DataModel model, int i)
		throws Exception {
	ItemSimilarity instance = null;
	switch (i) {
	case 0:
		instance = new TanimotoCoefficientSimilarity(model);
		break;
	case 1:
		instance = new EuclideanDistanceSimilarity(model);
		break;
	case 2:
		instance = new LogLikelihoodSimilarity(model);
		break;
	case 3:
		instance = new CityBlockSimilarity(model);
		break;
	case 4:
		instance = new UncenteredCosineSimilarity(model);
		break;
	}
	return instance;
}

测试：

public static void test(File dataFile) throws Exception {
	DataModel model = new FileDataModel(dataFile);
	for (int i = 0; i < 5; i++) {
		ItemSimilarity itemSimilarity = getISInstance(model, i);
		ItemBasedRecommender recommender = new GenericItemBasedRecommender(
				model, itemSimilarity);
		List<RecommendedItem> recommendations = recommender
				.mostSimilarItems(107, 1000);
		for (RecommendedItem recommendation : recommendations) {
			System.out.println(recommendation.getItemID() + "->"
					+ recommendation.getValue());
		}
		System.out.println("******************");
	}

}

效果对比：

推荐TanimotoCoefficientSimilarity和EuclideanDistanceSimilarity，使用场景不一样，大家根据效果进行选用