我正在尝试评估可能稀疏的数据集上的推荐算法，而我只有大约20 000个用户的341项。我只是想评估所有的相似度算法。我尝试了几乎所有基于用户的推荐，对于所有这些推荐，我从评估者那里获得了这个INFO，无论是哪一个，（AverageAbsoluteDifferenceRecommenderEvaluator或Root均方评分评估者）无法推荐在xXXX情况下。然而，最终输出仍然有一些结果。以下是我的评估员的输出：
3/06/17 14:11:35 INFO eval.AbstractDifferenceRecommenderEvaluator: Beginning evaluation using 0.7 of org.apache.mahout.cf.taste.impl.model.jdbc.PostgreSQLJDBCDataModel@44303e7b
13/06/17 14:15:17 INFO model.GenericDataModel: Processed 10000 users
13/06/17 14:15:17 INFO model.GenericDataModel: Processed 20000 users
13/06/17 14:15:17 INFO model.GenericDataModel: Processed 20530 users
13/06/17 14:15:17 INFO eval.AbstractDifferenceRecommenderEvaluator: Beginning evaluation of 11240 users
13/06/17 14:15:17 INFO eval.AbstractDifferenceRecommenderEvaluator: Starting timing of 11240 tasks in 4 threads
13/06/17 14:15:17 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 4ms
13/06/17 14:15:17 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 57MB / 101MB
13/06/17 14:15:17 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 3 cases
13/06/17 14:15:19 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 4ms
13/06/17 14:15:19 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 48MB / 99MB
13/06/17 14:15:19 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 882 cases
13/06/17 14:15:20 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:20 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 33MB / 109MB
13/06/17 14:15:20 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 1787 cases
13/06/17 14:15:22 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:22 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 41MB / 86MB
13/06/17 14:15:22 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 2687 cases
13/06/17 14:15:23 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:23 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 38MB / 98MB
13/06/17 14:15:23 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 3569 cases
13/06/17 14:15:24 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:24 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 28MB / 93MB
13/06/17 14:15:24 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 4465 cases
13/06/17 14:15:26 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:26 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 41MB / 88MB
13/06/17 14:15:26 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 5420 cases
13/06/17 14:15:27 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:27 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 45MB / 90MB
13/06/17 14:15:27 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 6317 cases
13/06/17 14:15:28 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:28 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 46MB / 103MB
13/06/17 14:15:28 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 7220 cases
13/06/17 14:15:30 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:30 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 72MB / 102MB
13/06/17 14:15:30 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 8145 cases
13/06/17 14:15:31 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:31 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 67MB / 99MB
13/06/17 14:15:31 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 9084 cases
13/06/17 14:15:33 INFO eval.AbstractDifferenceRecommenderEvaluator: Average time per recommendation: 5ms
13/06/17 14:15:33 INFO eval.AbstractDifferenceRecommenderEvaluator: Approximate memory used: 31MB / 83MB
13/06/17 14:15:33 INFO eval.AbstractDifferenceRecommenderEvaluator: Unable to recommend in 9982 cases
13/06/17 14:15:33 INFO eval.AbstractDifferenceRecommenderEvaluator: Evaluation result: 1.643042326271061
我不明白这些数字，为什么它们会显示这么多次，而且这在xxx案例中无法推荐大于我所有数据的20％？是否意味着对于一个用户而言，它不能在3个案例中推荐，而在9892中则不能推荐？