  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. In this study, we propose GRECS: a framework for adapting
GR to cold start recommendations. By utilizing explicit paths starting for
users rather than relying only on entity embeddings, GRECS can find items
corresponding to users' preferences by navigating the graph, even when limited
information about users is available. Our experiments show that GRECS mitigates
the cold start problem and outperforms competitive baselines across 5 standard
datasets while being explainable. This study highlights the potential of GR for
developing explainable recommender systems better suited for managing cold
users and items.
