Let us see how we can use Neo4j as the backbone to develop recommendation systems for different data scenarios. For this purpose, we will be using the collaborative filtering approach to process the data in hand and churn out relevant results.

In order to understand how the process works, let us use a simple data set of a dating site where you can sign in and view the profile of people who you could potentially date, and you can follow or like them, or vice versa. The graphical representation of such a dataset will represent the people as nodes, and the like operations from one person to another is represented as edges between them.

As shown in the following diagram, consider a user, John, who has just registered on the website and created a profile for himself. He begins browsing through profiles of women, searching for a person that might interest him. After going through the profiles of several people, he likes the profiles of three of them – Donna, Rose...