基于异构信息网络嵌入的专利技术主体间交易推荐模型

doi:10.3772/j.issn.1000-0135.2020.01.006

情报学报

2020, Vol. 39

Issue (1): 57-67 DOI: 10.3772/j.issn.1000-0135.2020.01.006

情报分析方法与技术

本期目录 | 过刊浏览 | 高级检索

基于异构信息网络嵌入的专利技术主体间交易推荐模型

何喜军, 董艳波, 武玉英, 蒋国瑞, 马珊, 郑瑶

北京工业大学经济与管理学院，北京 100124

Heterogeneous Information Network Embedding for Patent Technology Supply-Demand Trade Recommendations among Subjects

He Xijun, Dong Yanbo, Wu Yuying, Jiang Guorui, Ma Shan, Zheng Yao

School of Economics and Management, Beijing University of Technology, Beijing 100124

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摘要考虑专利技术主体间技术邻近、地理邻近、共申请关系、引证关系、经济圈效应、主体类型邻近、主体间从属关系这7种因素对交易的影响，构建由4类节点、10类关系组成的异构信息网络，设计基于元路径与元结构的异构关系遍历算法获取主体间关系序列。以关系序列为语料，构建基于网络嵌入的异构信息网络主体间交易推荐模型（PSR-vec），采用基于Huffman树的Skip-Gram方法进行网络嵌入训练，计算主体向量间相似度以实现交易推荐。通过2012—2018年电子信息领域专利数据的实证研究得出：第一，PSR-vec模型相比DeepWalk、node2vec与PathSim等方法，推荐精度大幅提高，达到82.4%；第二，融合多个元路径与元结构特征的推荐与单一特征相比，推荐精度大幅提高；第三，基于ρ2以及改进的元结构S4、S6、S8、S10、S12、S14的推荐结果均高于基于ρ1以及改进的元结构S3、S5、S7、S9、S11、S13的推荐精度，说明基于主体间转让技术邻近性的推荐精度更高；第四，在技术邻近元路径基础上分别融合主体间共申请、引证、从属、经济圈效应这4类邻近关系获得元结构并进行推荐，推荐精度均显著提高，而融合地理邻近、类型邻近2类关系后推荐精度有所降低，说明地理邻近、类型邻近对交易的促进作用不明显；第五，基于PSR-vec模型的推荐结果包括具有控股和供应等紧密关系的主体，也包括关系松散的主体，推荐结果具有多样性。本研究为专利技术主体间的有效对接提供了决策方法。

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	何喜军
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	郑瑶

关键词 ：交易推荐, 专利技术, 异构信息网络, 网络嵌入

收稿日期: 2019-02-27

基金资助:国家自然科学基金面上项目“异构信息网络下技术供需匹配模型与对接路径研究”（71974009）；北京市自然科学基金项目“基于技术交易数据的京津冀科技协同创新潜力挖掘”（9172002）。

作者简介: 何喜军，女，1979 年生，博士，副研究员，硕士生导师，主要研究领域为社会网络分析、复杂网络与区域协同创新；董艳波，男，1992 年生，硕士，主要研究领域为系统工程、文本挖掘、社会网络分析，E-mail：dyb@emails.bjut.edu.cn；武玉英，女，1966 年生，博士，副研究员，硕士生导师，主要研究领域为文本挖掘、社会网络分析；蒋国瑞，男，1954 年生，教授，硕士生导师，主要研究领域为管理信息系统与决策支持系统；马珊，女，1993 年生，硕士，主要研究领域为文本挖掘、社会网络；郑瑶，女，1996 年生，本科，主要研究领域为数学建模、信息计算。

引用本文:

何喜军, 董艳波, 武玉英, 蒋国瑞, 马珊, 郑瑶. 基于异构信息网络嵌入的专利技术主体间交易推荐模型[J]. 情报学报, 2020, 39(1): 57-67.
He Xijun, Dong Yanbo, Wu Yuying, Jiang Guorui, Ma Shan, Zheng Yao. Heterogeneous Information Network Embedding for Patent Technology Supply-Demand Trade Recommendations among Subjects. 情报学报, 2020, 39(1): 57-67.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2020.01.006 或 https://qbxb.istic.ac.cn/CN/Y2020/V39/I1/57

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