Building The Perfect Personalised Menu Using Python

How Gousto is building an algorithm to offer personalised menus to their customers using python

Irene Iriarte

Algorithms Case Study Data Science E-Commerce Machine-Learning

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This talk will describe how Gousto, a leading recipe box service based in the UK, is using python to build a personalisation ecosystem. Our menu planning optimisation algorithm allows us to create the perfect mix of recipes, ensuring a variety of dish types, cuisines and ingredients. Our recommendation engine sitting on top of this can then offer each customer a personally curated menu, making sure that users have meaningful choice. All this while ensuring that we are also optimising for maximum performance from the operations point of view!

To build this, we have used a range of Python packages, such as DEAP for implementing genetic algorithms, and integrations, such as the one for graph database neo4j.

The talk will give an overview of our methods, our infrastructure, our results and everything that we have learnt along the way!

Type: Talk (30 mins); Python level: Beginner; Domain level: Intermediate


Irene Iriarte

Gousto

Irene is a senior data scientist at recipe kit company Gousto. Since joining in 2016, she has been working on the development and implementation of algorithms, mainly focusing on forecasting and recommendations.

Before joining Gousto, Irene completed a PhD in Computational Chemistry at Imperial College London.