Datasets and Schemas

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

There are three types of datasets.

Users - This dataset stores user keys and metadata. Metadata can include data such as gender, age, or income bracket.
Items - This dataset stores item keys and metadata. Metadata can include data such as price, colour, or SKU.
Interactions - This stores the event data between your users and items.

Including metadata improves the accuracy of recommendations and is especially helpful for enhancing cold starts.

An application can have only one of each type of dataset. Before you create a dataset, you define the schema for that dataset. A schema tells Xineoh Consumer Behaviour API about the structure of your data and allows it to process the data correctly. A schema has a name key whose value must match the dataset type.

For an application, each dataset type has required-fields in the schema. Once you have defined the schema and created the dataset, you cannot make changes to the schema.

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