基于异构信息网络的推荐研究综述  

Review of recommendation based on heterogeneous information network

作　　者：汪春播 温继文[1] WANG Chun-bo;WEN Ji-wen(School of Economics and Management,Beijing Forestry University,Beijing 100083,China)

机构地区：[1]北京林业大学经济管理学院,北京100083

出　　处：《计算机工程与科学》2023年第11期2047-2059,共13页Computer Engineering & Science

摘　　要：推荐在满足用户信息需求和解决信息过载问题中发挥着重要作用,异构信息网络因其蕴含丰富的语义信息为推荐的优化提供了新的途径。在查阅国内外异构信息网络的推荐研究基础上,采用SATI、Ucinet、NetDraw和SPSS等软件进行文献计量分析和可视化分析,得出当前研究热点和进展。依据文献关键词的聚类结果,发现已有研究主要是基于聚类、随机游走、元路径、矩阵分解和网络嵌入的算法,并实现了在学术科研、兴趣点、Web服务、社交好友、专利交易和新闻等推荐场景的应用。基于异构信息网络的推荐研究还有较大发展空间,未来可以在动态推荐、深度网络表示学习和拓展应用等方面开展研究。Recommendation plays an important role in satisfying users needs of information and solving information overload.Heterogeneous information network contains rich semantics and provides a new way for recommendation optimization.Based on the research of recommendation on heterogeneous information network at home and abroad,this paper conducts bibliometric analysis and visual analysis with SATI,Ucinet,NetDraw,and SPSS to obtain the current research focuses and progress.According to the clustering results of literature keywords,the previous research are mainly based on clustering,random walk,meta-path,matrix factorization,network embedding algorithms,and applied in academic research,points of interest,Web services,social friends,patent trading,news and other recommendation scenarios.There is still a large development space for recommendation research based on heterogeneous information network.Future research can be carried out in dynamic recommendation,deep network representation learning and wider applications.

关 键 词：异构信息网络 推荐 元路径 网络嵌入 

分 类 号：G201[文化科学—传播学]