Case Study: Gousto achieves 30% more customers selecting recommended recipes with Neo4j graph database

Gousto Is Using Graph Technology to Personalize Its Ingredient Lists

Gousto, a UK recipe‑box company founded in 2012, needed to make an expanding menu (now 30 weekly recipes across new ranges) easier for customers to navigate and to understand the subtle drivers of food choice. With more options came the risk of decision overload, so Gousto sought a way to deliver highly relevant, personalized recipe and ingredient suggestions to keep convenience and choice in balance.

The company built a hybrid recommendation system using Neo4j and Cypher to model ingredient and recipe relationships, combining subscriber interaction data with recipe attributes to merge collaborative and content‑filtering approaches. The graph‑based solution gave designers deeper ingredient‑level insights, improved sourcing and cost control, and led to a 30% increase in customers selecting recommended recipes.

Gousto

Irene Iriarte Carretero

Data Scientist