Build Data

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

Open Navigation

You have now imported the data into the Xineoh API, and the next step is to process the data into the training and testing sets.

The training and testing data will be split into 80% training and 20% testing.

It is important to note here whether or not you want the data to be processed contextually or not, as this will affect the contextual parameter when you test the model later on.

Build Data and Structures for Training and Testing API Call

POST - /builder/build
Params: { version: v3.0 }
Body: { contextual: true }

We will be building it with the contextual field set to true. We are doing this because given the MovieLens use case it will create a more accurate model. For more on this param please see the official documentation here.

Successful Response:

{
    "status_code": 200,
    "success": "We will notify you when it's finished",
    "process_id": PROCESS_ID_HERE
}

When you receive a process_id from an API call, it means that the API is processing the request and that you will be emailed when it has been completed. You can view the progress of a process with the following API call.

View Process API Call:

GET - /process
PARAMS: { version: v3.0, process_id: PROCESS_ID_HERE }

Successful Response:

{
    "status_code": 200,
    "response": {
        "process_id": "PROCESS_ID_HERE",
        "status": "in_progress",
        "created_dt": "YEAR-MONTH-DAY HOUR:MINUTE:SECOND"
    }
}

You will receive the following email when the API has completed processing the data.

Email Notification:

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