为批量建议准备输入数据 - Amazon Personalize
Batch 推理作业输入和输出示例 JSON
Amazon Web Services 文档中描述的 Amazon Web Services 服务或功能可能因区域而异。要查看适用于中国区域的差异，请参阅 中国的 Amazon Web Services 服务入门 (PDF)。

本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

为批量建议准备输入数据

批量推理任务从 Amazon S3 存储桶导入您的批量输入JSON数据，使用您的自定义解决方案版本生成建议，然后将项目推荐导出到 Amazon S3 存储桶。您必须先准备好JSON文件并将其上传到 Amazon S3 存储桶，然后才能获得批量建议。我们建议您在 Amazon S3 存储桶中创建输出文件夹，或者使用单独的输出 Amazon S3 存储桶。然后，您可以使用相同的输入数据位置运行多个批量推理作业。

如果您使用带有占位符参数的过滤器（例如）$GENRE，则必须在输入JSON中为filterValues对象中的参数提供值。有关更多信息，请参阅 在输入中提供筛选器值 JSON。

准备和导入数据

  1. 根据您的食谱设置批量输入数据的格式。您无法通过 Trending-Now 食谱获得批量建议。

    • 对于 USER _ PERSONALIZATION 食谱和 Populity-Count 配方，您的输入数据是一个包含JSON以下列表的文件 userIds

    • 对于 RELATED _ ITEMS 食谱，您的输入数据是一个列表 itemIds

    • 对于 PERSONALIZED _ RANKING 食谱，您的输入数据是一个列表userIds，每个列表都与一个集合配对 itemIds

    用新行分隔每行。有关输入数据的示例，请参阅Batch 推理作业输入和输出示例 JSON。

  2. 将您的输入上传JSON到 Amazon S3 存储桶中的输入文件夹。有关更多信息，请参阅《Amazon Simple Storage Service 用户指南》中的使用拖放功能上传文件和文件夹。

  3. 为输出数据创建一个单独的位置，可以是文件夹，也可以是其他 Amazon S3 存储桶。通过为输出创建单独的位置JSON，您可以使用相同的输入数据位置运行多个批量推理作业。

  4. 创建批量推理作业。Amazon Personalize 会将解决方案版本的建议输出到输出数据位置。

Batch 推理作业输入和输出示例 JSON

如何将输入数据格式设置为您使用的食谱的格式。如果您使用带有占位符参数的过滤器（例如）$GENRE，则必须在输入JSON中为filterValues对象中的参数提供值。有关更多信息，请参阅 在输入中提供筛选器值 JSON。

以下各节列出了格式正确的批量推理作业的JSON输入和输出示例。您无法通过 Trending-Now 食谱获得批量建议。

USER_ PERSONALIZATION 食谱

下面显示了 USER _ PERSONALIZATION 配方格式正确的JSON输入和输出示例。如果您使用 Userpersonalization-v2，则每件推荐的商品都包含一份将该商品包含在推荐中的原因列表。此列表可以为空。有关可能原因的信息，请参阅 User-Personalization-v2 的推荐理由。

Input

用新行分隔每个 userId，如下所示。

{"userId": "4638"}
{"userId": "663"}
{"userId": "3384"}
...
Output
{"input":{"userId":"4638"},"output":{"recommendedItems":["63992","115149","110102","148626","148888","31685","102445","69526","92535","143355","62374","7451","56171","122882","66097","91542","142488","139385","40583","71530","39292","111360","34048","47099","135137"],"scores":[0.0152238,0.0069081,0.0068222,0.006394,0.0059746,0.0055851,0.0049357,0.0044644,0.0042968,0.004015,0.0038805,0.0037476,0.0036563,0.0036178,0.00341,0.0033467,0.0033258,0.0032454,0.0032076,0.0031996,0.0029558,0.0029021,0.0029007,0.0028837,0.0028316]},"error":null}
{"input":{"userId":"663"},"output":{"recommendedItems":["368","377","25","780","1610","648","1270","6","165","1196","1097","300","1183","608","104","474","736","293","141","2987","1265","2716","223","733","2028"],"scores":[0.0406197,0.0372557,0.0254077,0.0151975,0.014991,0.0127175,0.0124547,0.0116712,0.0091098,0.0085492,0.0079035,0.0078995,0.0075598,0.0074876,0.0072006,0.0071775,0.0068923,0.0066552,0.0066232,0.0062504,0.0062386,0.0061121,0.0060942,0.0060781,0.0059263]},"error":null}
{"input":{"userId":"3384"},"output":{"recommendedItems":["597","21","223","2144","208","2424","594","595","920","104","520","367","2081","39","1035","2054","160","1370","48","1092","158","2671","500","474","1907"],"scores":[0.0241061,0.0119394,0.0118012,0.010662,0.0086972,0.0079428,0.0073218,0.0071438,0.0069602,0.0056961,0.0055999,0.005577,0.0054387,0.0051787,0.0051412,0.0050493,0.0047126,0.0045393,0.0042159,0.0042098,0.004205,0.0042029,0.0040778,0.0038897,0.0038809]},"error":null}
...

以下显示了 Populity-Count 配方格式正确的JSON输入和输出示例。您无法通过 Trending-Now 食谱获得批量建议。

Input

用新行分隔每个 userId，如下所示。

{"userId": "12"}
{"userId": "105"}
{"userId": "41"}
...
Output
{"input": {"userId": "12"}, "output": {"recommendedItems": ["105", "106", "441"]}}
{"input": {"userId": "105"}, "output": {"recommendedItems": ["105", "106", "441"]}}
{"input": {"userId": "41"}, "output": {"recommendedItems": ["105", "106", "441"]}}
...

PERSONALIZED_ RANKING 食谱

下面显示了 PERSONALIZED _ recip RANKING es 的正确格式JSON输入和输出示例。

Input

用新行分隔每个 userId 和要排名的 itemIds 列表，如下所示。

{"userId": "891", "itemList": ["27", "886", "101"]}
{"userId": "445", "itemList": ["527", "55", "901"]}
{"userId": "71", "itemList": ["27", "351", "101"]}
...
Output
{"input":{"userId":"891","itemList":["27","886","101"]},"output":{"recommendedItems":["27","101","886"],"scores":[0.48421,0.28133,0.23446]}}
{"input":{"userId":"445","itemList":["527","55","901"]},"output":{"recommendedItems":["901","527","55"],"scores":[0.46972,0.31011,0.22017]}}
{"input":{"userId":"71","itemList":["29","351","199"]},"output":{"recommendedItems":["351","29","199"],"scores":[0.68937,0.24829,0.06232]}}
...

下面显示了 RELATED _ recip ITEMS es 的正确格式JSON输入和输出示例。

Input

用新行分隔每个 itemId，如下所示。

{"itemId": "105"}
{"itemId": "106"}
{"itemId": "441"}
...
Output
{"input": {"itemId": "105"}, "output": {"recommendedItems": ["106", "107", "49"]}}
{"input": {"itemId": "106"}, "output": {"recommendedItems": ["105", "107", "49"]}}
{"input": {"itemId": "441"}, "output": {"recommendedItems": ["2", "442", "435"]}}
...

下面显示了带有主题的 Similar-Items 配方的正确格式JSON输入和输出示例。

Input

用新行分隔每个 itemId，如下所示。

{"itemId": "40"}
{"itemId": "43"}
...
Output
{"input":{"itemId":"40"},"output":{"recommendedItems":["36","50","44","22","21","29","3","1","2","39"],"theme":"Movies with a strong female lead","itemsThemeRelevanceScores":[0.19994527,0.183059963,0.17478035,0.1618133,0.1574806,0.15468733,0.1499242,0.14353688,0.13531424,0.10291852]}}
{"input":{"itemId":"43"},"output":{"recommendedItems":["50","21","36","3","17","2","39","1","10","5"],"theme":"The best movies of 1995","itemsThemeRelevanceScores":[0.184988,0.1795761,0.11143453,0.0989443,0.08258403,0.07952615,0.07115086,0.0621634,-0.138913,-0.188913]}}
...