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.
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The first step in building a recommendation engine using graph ML is creating a data graph. This can be done using a graph database, such as Neo4j or JanusGraph, or by creating the graph in-memory using a graph library, such as NetworkX or igraph. The graph should include nodes for users and items, as well as edges that represent the relationships between them.
For example, if we are building a recommendation engine for a movie streaming platform, the graph would include nodes for users, movies, and actors, with edges representing the relationships between them (e.g., a user has watched a movie, a movie stars a particular actor).
