Building A Recommendation Engine Using Graph Machine Learning (GML)
“Recommender systems are the ultimate personalized marketing, a way to connect people with the products they are most likely to want, and to do it better than anyone else.”
— John Riedl , professor of computer science at the University of Minnesota and known as the “Father of Recommender Systems”.
Recommendation engines are an essential part of modern online platforms. They help users find content and products that are relevant to their interests, and they can also help businesses increase sales and engagement. One way to build a recommendation engine is by using graph-based machine learning (ML).
In this blog post, we will discuss how to build a recommendation engine using graph ML, including a detailed description of the process and properly formatted code examples.
Graph-based machine learning (ML) is a powerful approach for building recommendation engines as it allows one to model the relationships between different items and users in a comprehensive way. In graph ML, items and users are represented as nodes, and the relationships between them are represented as edges. By analyzing the patterns in these relationships, we can make recommendations for items that are likely to be of interest to a given user.
