基于属性异构网络表示学习的专利交易推荐

doi:10.3772/j.issn.1000-0135.2022.11.009

情报学报

2022, Vol. 41

Issue (11): 1214-1228 DOI: 10.3772/j.issn.1000-0135.2022.11.009

情报技术与应用

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基于属性异构网络表示学习的专利交易推荐

何喜军¹, 吴爽爽¹, 武玉英¹, 才久然¹, 庞婷^2,3, Chee SengChan²

1.北京工业大学经济与管理学院，北京 100124
2.马来亚大学计算机科学与信息技术学院人工智能系，吉隆坡 50603
3.新乡医学院网络与信息中心，新乡 453000

Recommendation of Patent Transaction Based on Attributed Heterogeneous Network Representation Learning

He Xijun¹, Wu Shuangshuang¹, Wu Yuying¹, Cai Jiuran¹, Pang Ting^2,3, Chee Seng Chan²

1.School of Economics and Management, Beijing University of Technology, Beijing 100124
2.Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603
3.Network and Information Center, Xinxiang Medical University, Xinxiang 453000

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摘要融合异构信息进行专利交易推荐可以促进交易，但存在因忽略专利属性而影响推荐结果的问题。本研究提出基于属性异构网络（attribute heterogeneous network，AHN）表示学习的专利交易推荐模型（patent transaction recommendation based on AHN representation learning，AHNRL-PTR）。首先筛选专利和组织中影响专利交易的属性；其次构建专利交易AHN，然后在AHN中引入网络表示学习，并基于多维高斯分布解决节点表示的不确定性，基于KL散度（Kullback-Leibler divergence）解决节点间距离非对称性。最后，以粤港澳大湾区有效发明授权专利数据进行实证研究，得出结论：第一，相比于metapath2vec、TADW（text-associated DeepWalk）和AHNRL-PTR模型的两个变体方法，AHNRL-PTR模型的推荐精度最高，超过86%，说明融合组织及专利属性，并聚焦节点表示的不确定性和非对称性问题的解决，能大幅提高推荐精度；第二，在非准确指标IntraSim和Popularity上，AHNRL-PTR的表现优于metapath2vec和两个变体方法，反映该方法的推荐结果具有一定的多样性，且可以挖掘推荐冷门专利；第三，基于两个非准确指标将组织聚类为六类，分别为中介型、领域骨干型、研究型、族群型、成长型、专业型，体现了推荐结果的可解释性和个性化水平。本研究可为专利交易智能化推荐服务提供决策支持。

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关键词 ：属性异构网络, 网络表示学习, 专利交易推荐

收稿日期: 2021-10-09

作者简介: 何喜军，女，1979年生，博士，教授，博士生导师，主要研究方向为复杂网络与区域协同创新；吴爽爽，女，1998年生，硕士研究生，主要研究方向为复杂网络，E-mail：woos_s@emails.bjut.edu.cn；武玉英，女，1966年生，博士，副教授，硕士生导师，主要研究方向为系统工程；才久然，女，1996年生，硕士研究生，主要研究方向为文本挖掘；庞婷，女，1989年生，博士研究生，主要研究方向为数据挖掘；Chee Seng Chan，男，1980年生，博士，副教授，博士生导师，主要研究方向为深度学习；

引用本文:

何喜军, 吴爽爽, 武玉英, 才久然, 庞婷, Chee SengChan. 基于属性异构网络表示学习的专利交易推荐[J]. 情报学报, 2022, 41(11): 1214-1228.
He Xijun, Wu Shuangshuang, Wu Yuying, Cai Jiuran, Pang Ting, Chee Seng Chan. Recommendation of Patent Transaction Based on Attributed Heterogeneous Network Representation Learning. 情报学报, 2022, 41(11): 1214-1228.

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https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2022.11.009 或 https://qbxb.istic.ac.cn/CN/Y2022/V41/I11/1214

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