Step 2: Data Transformation

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

Next, the data needs to be transformed into a CSV(comma-separated values) format that can be uploaded into your S3 bucket in the correct format.

Your CSVs should follow the schema of each dataset. For more information, please see the datasets and schemes section.

Below is an example of an ecommerce interactions schema and how the accompanying CSV will need to look.

Scheme used

{
	"name": "user_item_interaction",
	"namespace": "com.xineoh.recommender.schema",
	"fields": [{
		"name": "user_id",
		"type": "string"
	}, {
		"name": "item_id",
		"type": "integer"
	}, {
		"name": "event_type",
		"type": "string",
		"measurement": "ordinal"
	}, {
		"name": "event_value",
		"type": "float"
	}, {
		"name": "event_meta_data_1",
		"label": "selling_price",
		"type": "float",
		"recommender_field": 0
	}, {
		"name": "event_meta_data_2",
		"label": "cost",
		"type": "float",
		"recommender_field": 0
	}, {
		"name": "event_meta_data_3",
		"label": "invoice_id",
		"type": "float",
		"recommender_field": 0
	}, {
		"name": "event_date",
		"type": "timestamp"
	}],
	"version": "1.0"
}

CSV


"user_id","item_id","event_type","event_value","event_meta_data_1","event_meta_data_2","event_meta_data_3","event_date"
"4757","10053","quantity","15","113.466667","49.566667","3112572","2012-09-03 00:00:00"
"4757","301656","quantity","6","111.670000","52.385000","3112572","2012-09-03 00:00:00"
"4738","211","quantity","2","91.090000","37.945000","3112573","2012-09-03 00:00:00"
"4738","270","quantity","2","77.860000","29.760000","3112573","2012-09-03 00:00:00"
"4738","10050","quantity","3","111.180000","47.785000","3112573","2012-09-03 00:00:00"
"4424","382","quantity","4","107.780000","95.800000","3112574","2012-09-03 00:00:00"
"12091","1","quantity","19","110.690000","62.652500","3112575","2012-09-03 00:00:00"
...

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