The text discusses building a recommendation engine using Neo4j, a graph database. The author aims to create a system that recommends items from a menu based on user preferences and order history. The system will use two types of filtering: collaborative filtering and content-based filtering. Collaborative filtering is based on the idea that users with similar interests will also like similar items, while content-based filtering is based on the idea that people generally prefer certain types of products or services. The author designs a data model to store user orders, menu items, and relationships between them. The system will recommend items based on a user's order history and information about co-occurrence of items among all users. The goal is to provide personalized recommendations to users when they open the app or website.