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Introduction
How It Works
Prerequisites
Administrator Account and Organisation Creation Application Creation Data Set Requirements
Step 1: Data Cleanup Step 2: Data Transformation Step 3: Upload to S3 Bucket Step 4: Sharing S3 Bucket Access to Xineoh’s API
Applications, Datasets and Schemas
Applications
Application Types
Datasets and Schemas
Minimum Requirements for Datasets and Schemas Customising Datasets and Schemas
Steps in Building a Recommender with the API
Step 1: Batch Upload Step 2: Build Data and Structures for Training and Testing Step 3: Train Model Step 4: Test Model Step 5: Optimise Meta Parameters Step 6: User-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 7: Item-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 8: Get Recommendations
Sample Implementation - Movielens 25M Dataset
S3 Bucket Creation Login as Organisation First Application Download MovieLens Preprocessed Data Upload Data to S3 Bucket Building the Recommender
Uploading S3 Bucket data to Xineoh Platform Build Data Train Test Optimization User Clustering and Trade-Off Item Clustering and Trade-Off
First Recommendations
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2. Application Creation

You will need to create an application to act as a wrapper for the datasets to get started. Only one dataset should be used per application. For example, one application will be used for the MovieLens 25M Data Set. One application will be used for each of your company’s eCommerce stores, media sites, etc.

There are two application types, media and ecommerce. When creating an application in the following steps please ensure that you choose the application the appropriate type.

The steps below outline how to create an application:

  1. Go to our organisation page.
  2. Click the “Add Application” button. This can be found on the right hand side of the applications table.
  3. Submit the add application form.

Please note that when filling in the form you will need to provide the S3 bucket access key. This is the S3 access key of the bucket that will hold your datasets and schemas.

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