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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Sample Implementation - MovieLens 25M Dataset

Introduction

In this step-by-step guide you will go through all the steps required to create a Xineoh Recommender. You will be using a preprocessed MovieLens 25 million dataset. You will build, train, test and optimise your recommendation models. After optimising you will cluster the items and users from the dataset. Finally, you will deploy your model to get your very first set of recommendations.

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