数字图书馆论坛2025，Vol.21Issue(5)：11-19,9.DOI:10.3772/j.issn.1673-2286.2025.05.002
基于精细异质信息网络表示学习的文献推荐研究
Literature Recommendation Based on Representation Learning of Fine-Grained Heterogeneous Information Network
摘要
Abstract
Current research on literature recommendation based on heterogeneous information network are primarily based on existing citations,co-authorships,and publications in the same journals to build the relationship between literature.These methods do not build relationships from the perspectives of the research questions and methodological models of the literature,which are the finer-grained contents that researchers are more concerned about.This limitation prevents the integration of these fine-grained relationships into the literature recommendation process,thereby affecting the final recommendation effectiveness.This paper incorporates two types of fine-grained labels,namely research questions and methodological models of literature,into an academic information network and proposes a literature recommendation model called PRM-FHIN.It optimizes the HECO approach for heterogeneous information network representation learning to acquire structural vectors of network nodes and fine-tunes the SciBERT model to learn content vectors of network nodes.The final literature recommendation is achieved based on the integration of these content vectors and structural vectors.Using 1.85 million papers in the field of computer science from 2010 to 2020 extracted from the Open Academic Graph as experimental data,the results show that the optimized HECO algorithm can better embed the network nodes.Incorporating fine-grained labels such as research questions and methodological models into the heterogeneous information network enriches the semantic information between the literature,thereby improving the final literature recommendation effectiveness.关键词
精细异质信息网络/异质信息网络表示学习/文献推荐/细粒度标签/计算机科学/研究问题/方法模型Key words
Fine-Grained Heterogeneous Information Network/Representation Learning of Heterogeneous Information Network/Literature Recommendation/Fine-Grained Label/Computer Science/Research Question/Methodological Model分类
社会科学引用本文复制引用
李琳娜,郭晓琪,张运良,王力,张晓丹..基于精细异质信息网络表示学习的文献推荐研究[J].数字图书馆论坛,2025,21(5):11-19,9.基金项目
本研究得到中国科学技术信息研究所重点工作项目"面向战略决策的智能情报技术引擎研究及应用"(编号:ZD20250-08)资助. （编号:ZD20250-08）
