# Preparing input data for batch recommendations A batch inference job imports your batch input JSON data from an Amazon S3 bucket, uses your custom solution version to generate recommendations, and then exports the item recommendations to an Amazon S3 bucket. Before you can get batch recommendations, you must prepare and upload your JSON file to an Amazon S3 bucket. We recommend that you create an output folder in your Amazon S3 bucket or use a separate output Amazon S3 bucket. You can then run multiple batch inference jobs using the same input data location. If you use a filter with placeholder parameters, such as `$GENRE`, you must provide the values for the parameters in a `filterValues` object in your input JSON. For more information, see [Providing filter values in your input JSON](filter-batch.md#providing-filter-values). **To prepare and import data** 1. Format your batch input data depending on your recipe. You can't get batch recommendations with the Trending-Now recipe. + For USER\$1PERSONALIZATION recipes and the Popularity-Count recipe, your input data is a JSON file with a list of userIds + For RELATED\$1ITEMS recipes, your input data is a list of itemIds + For PERSONALIZED\$1RANKING recipes, your input data is a list of userIds, each paired with a collection of itemIds Separate each row with a new line. For input data examples, see [Batch inference job input and output JSON examples](#batch-inference-job-json-examples). 1. Upload your input JSON to an input folder in your Amazon S3 bucket. For more information, see [Uploading files and folders by using drag and drop](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/upload-objects.html) in the *Amazon Simple Storage Service User Guide* 1. Create a separate location for your output data, either a folder or a different Amazon S3 bucket. By creating a separate location for the output JSON, you can run multiple batch inference jobs with the same input data location. 1. Create a batch inference job. Amazon Personalize outputs the recommendations from your solution version to your output data location. ## Batch inference job input and output JSON examples How you format your input data the recipe you use. If you use a filter with placeholder parameters, such as `$GENRE`, you must provide the values for the parameters in a `filterValues` object in your input JSON. For more information, see [Providing filter values in your input JSON](filter-batch.md#providing-filter-values). The following sections list correctly formatted JSON input and output examples for batch inference jobs. You can't get batch recommendations with the Trending-Now recipe. **Topics** + [USER\$1PERSONALIZATION recipes](#batch-input-user-personalization) + [POPULAR\$1ITEMS recipes (Popularity-Count only)](#batch-input-popular-items) + [PERSONALIZED\$1RANKING recipes](#batch-input-ranking) + [RELATED\$1ITEMS recipes](#batch-input-related-items) ### USER\$1PERSONALIZATION recipes The following shows correctly formatted JSON input and output examples for the USER\$1PERSONALIZATION recipes. If you use User-Personalization-v2, each recommended item includes a list of reasons for why the item was included in recommendations. This list can be empty. For information about possible reasons, see [Recommendation reasons with User-Personalization-v2](recommendations.md#recommendation-reasons). ------ #### [ Input ] Separate each `userId` with a new line as follows. ``` {"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} ... ``` ------ ### POPULAR\$1ITEMS recipes (Popularity-Count only) The following shows correctly formatted JSON input and output examples for the Popularity-Count recipe. You can't get batch recommendations with the Trending-Now recipe. ------ #### [ Input ] Separate each `userId` with a new line as follows. ``` {"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\$1RANKING recipes The following shows correctly formatted JSON input and output examples for PERSONALIZED\$1RANKING recipes. ------ #### [ Input ] Separate each `userId` and list of `itemIds` to be ranked with a new line as follows. ``` {"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]}} ... ``` ------ ### RELATED\$1ITEMS recipes The following shows correctly formatted JSON input and output examples for RELATED\$1ITEMS recipes. ------ #### [ Input ] Separate each `itemId` with a new line as follows. ``` {"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"]}} ... ``` ------ The following shows correctly formatted JSON input and output examples for the Similar-Items recipe with themes. ------ #### [ Input ] Separate each `itemId` with a new line as follows. ``` {"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]}} ... ``` ------