论文标题
基于图形的推荐系统的调查
A Survey on Knowledge Graph-Based Recommender Systems
论文作者
论文摘要
为了解决信息爆炸问题并增强在各种在线应用程序中的用户体验，已开发了推荐系统来建模用户偏好。尽管为更个性化的建议做出了许多努力，但推荐系统仍然面临一些挑战，例如数据稀疏和冷启动。近年来，以知识图作为附带信息生成建议引起了人们的极大兴趣。这种方法不仅可以减轻上述问题以获得更准确的建议，还可以为推荐物品提供解释。在本文中，我们对基于图形的推荐系统进行了系统的调查。我们收集了最近在该领域发表的论文，并从两个角度总结了它们。一方面，我们通过关注论文如何利用知识图来准确且可解释的建议来研究所提出的算法。另一方面，我们介绍了这些作品中使用的数据集。最后，我们提出了该领域的几个潜在研究方向。
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.
