Graph Reasoning for Explainable Cold Start Recommendation

Understand

The cold start problem, where new users or items have no interaction history, remains a critical challenge in recommender systems (RS).

• A common solution involves using Knowledge Graphs (KG) to train entity embeddings or Graph Neural Networks (GNNs).
• Since KGs incorporate auxiliary data and not just user/item interactions, these methods can make relevant recommendations for cold users or items.
• Graph Reasoning (GR) methods, however, find paths from users to items to recommend using relations in the KG and, in the context of RS, have been used for interpretability.