Training graph deep learning models

Training graph deep learning models for recommendation systems is a complex process that requires various factors to be considered carefully. In this section, we’ll provide a comprehensive guide to training these models.

Data preprocessing

Effective data preprocessing is crucial for the success of graph-based recommendation models. Let’s dive deeper into the steps involved.

Building the interaction graph

Creating a high-quality interaction graph is the foundation of graph-based recommendation systems. Let’s take a look:

1. Node creation:
   • Assign unique identifiers to each user and movie.
   • Create node attributes to store relevant information.
2. Edge creation:
   • Create edges based on user-movie interactions.
   • Consider different types of interactions (for example, ratings, views, and likes).
3. Edge weighting:
   • Assign weights to edges based on interaction strength.
   • Normalize weights to ensure consistency across different...