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# Mempersiapkan data input untuk rekomendasi batch
Pekerjaan inferensi batch mengimpor data JSON masukan batch Anda dari bucket Amazon S3, menggunakan versi solusi kustom Anda untuk menghasilkan rekomendasi, lalu mengekspor rekomendasi item ke bucket Amazon S3. Sebelum Anda bisa mendapatkan rekomendasi batch, Anda harus menyiapkan dan mengunggah file JSON Anda ke bucket Amazon S3. Sebaiknya buat folder keluaran di bucket Amazon S3 atau gunakan bucket Amazon S3 keluaran terpisah. Anda kemudian dapat menjalankan beberapa pekerjaan inferensi batch menggunakan lokasi data input yang sama.
Jika Anda menggunakan filter dengan parameter placeholder, seperti`$GENRE`, Anda harus memberikan nilai untuk parameter dalam `filterValues` objek di JSON input Anda. Untuk informasi selengkapnya, lihat [Memberikan nilai filter di JSON masukan Anda](filter-batch.md#providing-filter-values).
**Untuk menyiapkan dan mengimpor data**
1. Format data input batch Anda tergantung pada resep Anda. Anda tidak bisa mendapatkan rekomendasi batch dengan resep Trending-Now.
+ Untuk resep USER\$1PERSONALIZATION dan resep Popularity-Count, data input Anda adalah file JSON dengan daftar UserIds
+ Untuk resep RELATED\$1ITEMS, data masukan Anda adalah daftar ItemIds
+ Untuk resep PERSONALIZED\$1RANKING, data masukan Anda adalah daftar UserIds, masing-masing dipasangkan dengan koleksi ItemIds
Pisahkan setiap baris dengan baris baru. Untuk contoh data masukan, lihat[Contoh input dan keluaran pekerjaan inferensi Batch JSON](#batch-inference-job-json-examples).
1. Unggah JSON masukan Anda ke folder input di bucket Amazon S3 Anda. Untuk informasi selengkapnya, lihat [Mengunggah file dan folder menggunakan seret dan lepas](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/upload-objects.html) di *Panduan Pengguna Layanan Penyimpanan Sederhana Amazon*
1. Buat lokasi terpisah untuk data keluaran Anda, baik folder atau bucket Amazon S3 lainnya. Dengan membuat lokasi terpisah untuk JSON keluaran, Anda dapat menjalankan beberapa pekerjaan inferensi batch dengan lokasi data input yang sama.
1. Buat pekerjaan inferensi batch. Amazon Personalize mengeluarkan rekomendasi dari versi solusi Anda ke lokasi data keluaran Anda.
## Contoh input dan keluaran pekerjaan inferensi Batch JSON
Bagaimana Anda memformat data input Anda resep yang Anda gunakan. Jika Anda menggunakan filter dengan parameter placeholder, seperti`$GENRE`, Anda harus memberikan nilai untuk parameter dalam `filterValues` objek di JSON input Anda. Untuk informasi selengkapnya, lihat [Memberikan nilai filter di JSON masukan Anda](filter-batch.md#providing-filter-values).
Bagian berikut mencantumkan contoh input dan output JSON yang diformat dengan benar untuk pekerjaan inferensi batch. Anda tidak bisa mendapatkan rekomendasi batch dengan resep Trending-Now.
**Topics**
+ [Resep USER\$1PERSONALIZATION](#batch-input-user-personalization)
+ [Resep POPULAR\$1ITEMS (Hanya Hitungan Popularitas)](#batch-input-popular-items)
+ [resep PERSONALIZED\$1RANKING](#batch-input-ranking)
+ [Resep RELATED\$1ITEMS](#batch-input-related-items)
### Resep USER\$1PERSONALIZATION
Berikut ini menunjukkan contoh input dan output JSON yang diformat dengan benar untuk resep USER\$1PERSONALIZATION. Jika Anda menggunakan User-Personalization-v 2, setiap item yang direkomendasikan mencakup daftar alasan mengapa item tersebut dimasukkan dalam rekomendasi. Daftar ini bisa kosong. Untuk informasi tentang kemungkinan alasan, lihat[Alasan rekomendasi dengan User-Personalization-v 2](recommendations.md#recommendation-reasons).
------
#### [ Input ]
Pisahkan masing-masing `userId` dengan baris baru sebagai berikut.
```
{"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}
...
```
------
### Resep POPULAR\$1ITEMS (Hanya Hitungan Popularitas)
Berikut ini menunjukkan contoh input dan output JSON yang diformat dengan benar untuk resep Popularity-Count. Anda tidak bisa mendapatkan rekomendasi batch dengan resep Trending-Now.
------
#### [ Input ]
Pisahkan masing-masing `userId` dengan baris baru sebagai berikut.
```
{"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"]}}
...
```
------
### resep PERSONALIZED\$1RANKING
Berikut ini menunjukkan contoh input dan output JSON yang diformat dengan benar untuk resep PERSONALIZED\$1RANKING.
------
#### [ Input ]
Pisahkan masing-masing `userId` dan daftar `itemIds` yang akan diberi peringkat dengan baris baru sebagai berikut.
```
{"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]}}
...
```
------
### Resep RELATED\$1ITEMS
Berikut ini menunjukkan contoh input dan output JSON yang diformat dengan benar untuk resep RELATED\$1ITEMS.
------
#### [ Input ]
Pisahkan masing-masing `itemId` dengan baris baru sebagai berikut.
```
{"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"]}}
...
```
------
Berikut ini menunjukkan contoh input dan output JSON yang diformat dengan benar untuk resep Similar-Items dengan tema.
------
#### [ Input ]
Pisahkan masing-masing `itemId` dengan baris baru sebagai berikut.
```
{"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]}}
...
```
------