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1
Introduction
2
About Gusto
3
Agenda
4
Overview
5
Deciding whats on the menu
6
Menu size
7
Planning menus
8
The problem
9
Datadriven menu
10
Genetic algorithm
11
Objectives
12
Minimising Unique Ingredients
13
Results
14
Personalization
15
Contentbased Recommendations
16
Collaborative Filtering
17
Personalization Results
18
Customer Feedback
19
Recipe Development
20
Lessons Learned
21
Outro
Description:
Discover how a leading UK recipe box service utilizes Python to create personalized menus in this 21-minute conference talk from EuroPython 2020. Learn about the implementation of a menu planning optimization algorithm that ensures a diverse mix of recipes, dish types, cuisines, and ingredients. Explore the development of a recommendation engine that offers customers individually curated menus while optimizing operational performance. Gain insights into the use of Python packages like DEAP for genetic algorithms and integrations with graph databases such as neo4j. Delve into the methods, infrastructure, results, and lessons learned in building a personalization ecosystem for recipe selection and menu planning.

Building the Perfect Personalised Menu Using Python

EuroPython Conference
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