Graph-based recommendation models
Graph-based recommendation models have emerged as powerful tools for capturing complex relationships between users and items in recommendation systems. These models leverage the rich structural information inherent in user-item interaction graphs to generate more accurate and personalized recommendations. In this section, we’ll explore three major categories of graph-based recommendation models, starting with matrix factorization (MF) with graph regularization.
MF with graph regularization
MF is a fundamental technique in CF, and its integration with graph structures has led to significant improvements in recommendation quality. Graph regularization in MF models helps to incorporate the structural information of the user-item interaction graph into the learning process.
The basic idea is to add a regularization term to the traditional MF objective function that encourages connected nodes in the graph to have similar latent representations...
