我正在尝试在我的数据集上评估推荐算法，这可能是稀疏的，我只有 341 个项目用于大约 20 000 个用户。我只想评估所有的相似性算法。我尝试了几乎所有基于用户的推荐，对于所有这些我都从评估者那里获得了这个信息，无论是哪一个（AverageAbsoluteDifferenceRecommenderEvaluator或均方根评分评估者）在 xXXX 案例中都无法推荐。然而最终的输出仍然有一些结果。这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，为什么它们显示了这么多次，这是否无法在 xxx 案例中推荐大于我所有数据的 20%？这是否意味着对于一个用户在 3 种情况下不能推荐，而在 9892 中不能推荐其他用户？