Real-world applications
In this section, we will explore some areas where graph learning is actively being applied.
Recommender systems
Graph learning has emerged as a powerful tool in the field of recommendation systems, enhancing their capabilities and effectiveness. Recommendation systems aim to predict user preferences and provide personalized suggestions, and graph learning leverages the inherent relational structure of data to achieve this more efficiently.
Figure 2.11 – User-item link prediction for recommendation
Figure 2.11 shows how we can translate a user-item affinity task for an e-commerce recommendation to a link prediction problem. This task is an example of one area, among others, in recommendation systems where graph learning can play a significant role.
User-item graph representation
Graph learning enables the representation of users and items as nodes in a graph, with edges indicating interactions or relationships...
