Explainability in graph-based recommendations
As recommendation systems become more sophisticated, the need for explainable AI (XAI) in these systems grows. Graph-based models offer unique opportunities for enhancing the explainability of recommendations.
Attention mechanisms for interpretability
As we saw in Chapter 4, graph attention networks (GATs) can be leveraged to provide insights into which nodes or features contribute most to a recommendation. For movie recommendations, this could reveal which actors, directors, or genres have the most significant influence on a user’s preferences.
Consider a user who frequently watches action movies starring Tom Cruise. The attention mechanism might highlight that the presence of Tom Cruise in a movie’s cast graph node has a higher weight in the recommendation process for this user compared to other factors.
Path-based explanations
Metapath-based models can offer intuitive explanations by showing the reasoning...
