Optimization - Getting Started | Xineoh – Predicting Consumer Behavior with AI

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

Optimisation

The next step is to optimise the model through hyperparameter optimisation.

To optimise the model, you must call the Optimize Meta Parameters API Call specifying the number of hyperparameter combinations to try.

Optimise Meta Parameters API Call

POST - /builder/optimize
Params: { version: v3.0 }
Body: { max_iter: 50 }

For most users 50 iterations is enough to find the optimal parameters.

Optimisation can take varying amounts of time, ranging from 30 minutes to a couple of hours.

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