1. Personalized recommendations using AWS Forecast


    The following Pulumi program in C# sets up an AWS Forecast Dataset, which is the first step towards building a predictive model for generating personalized recommendations.

    Please keep in mind that this is a simple example and you'll need to modify it to suit your exact requirements, such as changing the dataset details (schema, dataFrequency, domain, etc.) or adding more AWS Forecast resources (e.g., predictors, forecasts).

    using Pulumi; using Aws = Pulumi.Aws; class MyStack : Stack { public MyStack() { var dataset = new Aws.Forecast.Dataset("dataset", new Aws.Forecast.DatasetArgs { DataFrequency = "H", // Possibly change, depending on your requirements DatasetName = "my-dataset", // Provide the name of your dataset DatasetType = "TARGET_TIME_SERIES", Domain = "RETAIL", // Specify the domain according to your needs Schema = new Aws.Inputs.Forecast.DatasetSchemaArgs { Attributes = // Adjust these attributes based on your own dataset { new Aws.Inputs.Forecast.DatasetSchemaAttributeArgs { AttributeName = "timestamp", AttributeType = "timestamp" }, new Aws.Inputs.Forecast.DatasetSchemaAttributeArgs { AttributeName = "item_id", AttributeType = "string" }, new Aws.Inputs.Forecast.DatasetSchemaAttributeArgs { AttributeName = "demand", AttributeType = "float" } } } }); this.RegisterOutputs(new { DatasetArn = dataset.Arn }); } }

    AWS Forecast uses machine learning to combine time series data with additional variables to build forecasts. The dataset you provide will influence the quality of your forecast: for example, to build personalized recommendations you will probably need historical purchase data and may also want to use additional information about the users or items in your catalog.

    Refer to the official AWS Forecast documentation and the Pulumi documentation for the aws.forecast.Dataset resource for more details.

    This Pulumi program only creates the dataset resource. To utilize AWS Forecast for personalized recommendations, you would typically follow these steps:

    1. Create a Dataset (accomplished by the above Pulumi program).
    2. Import the data into your Dataset from S3.
    3. Train a Predictor using your Dataset.
    4. Generate a Forecast from your Predictor.
    5. Query the Forecast for your application.

    Steps 2-5 involve more AWS Forecast resources, and are beyond the scope of your original question. If you need help creating those resources with Pulumi, please let me know!