{"id":344,"date":"2020-10-03T02:38:55","date_gmt":"2020-10-03T02:38:55","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/10\/03\/simplify-data-management-with-new-apis-in-amazon-personalize\/"},"modified":"2020-10-03T02:38:55","modified_gmt":"2020-10-03T02:38:55","slug":"simplify-data-management-with-new-apis-in-amazon-personalize","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/10\/03\/simplify-data-management-with-new-apis-in-amazon-personalize\/","title":{"rendered":"Simplify data management with new APIs in Amazon Personalize"},"content":{"rendered":"
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Amazon Personalize<\/a> now makes it easier to manage your growing item and user catalogs with new APIs to incrementally add items and users in your datasets to create personalized recommendations. With the new putItems<\/code> and putUsers<\/code> APIs, you can simplify the process of managing your datasets. You no longer need to upload an entire dataset containing historical records and new records just to include new records in your recommendations. Providing new records to Amazon Personalize when they become available reduces your latency for incorporating new information, ensuring your recommendations remain relevant to your users and item catalog.<\/p>\n

Based on over 20 years of personalization experience at Amazon.com, Amazon Personalize enables you to improve customer engagement by powering personalized product and content recommendations and targeted marketing promotions. Amazon Personalize uses machine learning (ML) to create higher-quality recommendations for your websites and applications. You can get started without any prior ML experience and use simple APIs to easily build sophisticated personalization capabilities in just a few clicks. Amazon Personalize processes and examines your data, identifies what is meaningful, and trains and optimizes a personalization model that is customized for your data. All your data is encrypted to be private and secure, and is only used to create recommendations for your users.<\/p>\n

This post walks you through the process of incrementally modifying your items and users datasets in Amazon Personalize.<\/p>\n

Adding new items and users to your datasets<\/h2>\n

For this use case, we create a dataset group with an interaction dataset, an item dataset (item metadata) and a user dataset using the Amazon Personalize CLI. For instructions on creating a dataset group, see Getting Started (CLI)<\/a>.<\/p>\n

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  1. Create an Interactions<\/code> dataset using the following schema and import data using the interactions-100k.csv<\/a> data file:<\/li>\n<\/ol>\n
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    {\r\n\t\"type\": \"record\",\r\n\t\"name\": \"Interactions\",\r\n\t\"namespace\": \"com.amazonaws.personalize.schema\",\r\n\t\"fields\": [\r\n\t\t{\r\n\t\t\t\"name\": \"USER_ID\",\r\n\t\t\t\"type\": \"string\"\r\n\t\t},\r\n\t\t{\r\n\t\t\t\"name\": \"ITEM_ID\",\r\n\t\t\t\"type\": \"string\"\r\n\t\t},\r\n\t\t{\r\n\t\t\t\"name\": \"EVENT_TYPE\",\r\n\t\t\t\"type\": [\r\n\t\t\t\t\"string\"\r\n\t\t\t]\r\n\t\t},\r\n\t\t{\r\n\t\t\t\"name\": \"EVENT_VALUE\",\r\n\t\t\t\"type\": [\r\n\t\t\t\t\"null\",\r\n\t\t\t\t\"float\"\r\n\t\t\t]\r\n\t\t},\r\n\t\t{\r\n\t\t\t\"name\": \"TIMESTAMP\",\r\n\t\t\t\"type\": \"long\"\r\n\t\t}\r\n\t]\r\n}\r\n<\/code><\/pre>\n<\/div>\n
      \n
    1. Create an Items dataset using the following schema and import data using the csv<\/a> data file:<\/li>\n<\/ol>\n
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      {\r\n\t\"type\": \"record\",\r\n\t\"name\": \"Items\",\r\n\t\"namespace\": \"com.amazonaws.personalize.schema\",\r\n\t\"fields\": [\r\n\t\t{\r\n\t\t\t\"name\": \"ITEM_ID\",\r\n\t\t\t\"type\": \"string\"\r\n\t\t},\r\n\t\t{\r\n\t\t\t\"name\": \"GENRE\",\r\n\t\t\t\"type\u201d: \"null\u201d\r\n\t\t\t \"categorical\": true\r\n\t\t}\r\n\t],\r\n\t\"version\": \"1.0\"\r\n}\r\n<\/code><\/pre>\n<\/div>\n
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      1. Create a Users dataset using the following schema and import data using the csv<\/a> data file:<\/li>\n<\/ol>\n
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        {\r\n\t\"type\": \"record\",\r\n\t\"name\": \"Users\",\r\n\t\"namespace\": \"com.amazonaws.personalize.schema\",\r\n\t\"fields\": [\r\n\t\t{\r\n\t\t\t\"name\": \"USER_ID\",\r\n\t\t\t\"type\": \"string\"\r\n\t\t},\r\n\t\t{\r\n\t\t\t\"name\": \"AGE\",\r\n\t\t\t\"type\": \"int\"\r\n\t\t},\r\n\t\t{\r\n\t\t\t\"name\": \"GENDER\",\r\n\t\t\t\"type\": \"string\"\r\n\t\t}\r\n\t],\r\n\t\"version\": \"1.0\"\r\n}\r\n<\/code><\/pre>\n<\/div>\n
        Now that you have created your datasets, you can add data to them in two different ways:<\/p>\n