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.

翻译：冷启动问题（即新用户或物品缺乏交互历史）始终是推荐系统面临的关键挑战。常见解决方案包括利用知识图谱训练实体嵌入或图神经网络。由于知识图谱整合了辅助数据而不仅限于用户/物品交互，这些方法能够为冷启动用户或物品生成相关推荐。然而，图推理方法通过寻找知识图谱中从用户到物品的关系路径进行推荐，在推荐系统领域已被应用于可解释性研究。本研究提出GRECS框架，将图推理技术适配于冷启动推荐场景。该框架通过显式构建从用户出发的路径（而非仅依赖实体嵌入），即使在用户信息有限的情况下，也能通过图谱导航找到符合用户偏好的物品。实验表明，GRECS在五个标准数据集上有效缓解冷启动问题，其性能优于现有基线方法且具备可解释性。本研究揭示了图推理技术在开发更适合处理冷启动用户与物品的可解释推荐系统方面的潜力。