利用知识图谱的推荐系统研究综述

Review of Recommendation Systems Using Knowledge Graph

随着互联网的快速发展,如何从海量数据中筛选实际需要的信息变得尤为重要。推荐系统作为一种信息过滤的方法,旨在为用户推荐个性化内容。传统推荐算法中普遍存在数据稀疏和冷启动问题,近年来,研究者利用知识图谱中丰富的实体与关系信息,不仅能够缓解以上问题,同时增强了推荐系统的整体性能。利用知识图谱的推荐系统研究主要包括三方面内容:介绍推荐系统和知识图谱的基本概念,指出现有推荐算法的不足之处;根据不同核心技术详细分析利用知识图谱的推荐系统研究现状,评估不同方法的技术优势与挑战;总结相关应用场景和数据集信息,并展望未来发展前景。

With the rapid development of the Internet,how to obtain the needed information from huge amounts of data becomes more important.The recommendation system is a method of screening information,which aims to recommend personalized content for users.However,traditional recommendation algorithms still suffer from several challenges,such as data sparsity and cold start.In recent years,researchers have used the rich entity and relationship information in the knowledge graph to alleviate the above problems.The overall performance of the recommendation system is enhanced.This paper gives a review of the recommendation system based on knowledge graph from three aspects:Firstly,basic concepts of the recommendation system and knowledge graph are introduced.The shortcomings of the existing recommendation algorithms are pointed out.Then,the research of the recommendation system based on knowledge graph is analyzed in detail.The advantages and challenges of the different approaches are assessed.Finally,relevant application scenarios and future development prospects are summarized.