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| 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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Abstract We constructed a heterogeneous information network (HIN) consisting of four types of nodes and ten types of relationships after considering the impact of seven parameters (technological proximity, geographical proximity, co-application relationship, citation relation, economic circle effect, subject type proximity, and subordinate relation among subjects) on trade. We then proposed heterogeneous relationship traversal algorithms based on the meta path and meta structure and obtained subject-relationship sequences based on multi-relational mapping. Based on the relation sequence corpus, we constructed a model of patent technology subject trade recommendations using network embedding (PSR-vec). We then trained the model using the Skip-Gram method of the Huffman tree to obtain a subject vector space representation. Finally, we calculated the similarity between subjectsvectors to formulate a trade recommendation. Through empirical research on patent data in the electronic information field from 2012 to 2018, we found that, first, with an accuracy rate of 82.4%, the PSR-vec model had greater accuracy compared to the DeepWalk, node2vec, and PathSim methods. Second, the recommendation accuracy of the combination of multiple meta paths and meta structures was higher than that of the single meta path or meta structure recommendation. Third, the accuracy of the recommendation results based on ρ2 and the meta structures S4, S6, S8, S10, S12, and S14 is higher than the accuracy of those based on ρ1 and improved meta-structures S3、S5、S7、S9、S11, and S13. This shows that the recommendation accuracy based on technological proximity among subjects is higher. Fourth, the recommendation accuracy is significantly improved for the meta structures with co-application, citation, subordinate relation, and economic circle effect on the basis of technological proximity, while the recommendation accuracy is lower when combining geographical proximity and subject type proximity. This indicates that geographical proximity and subject type proximity do not promote patent technology trade. Fifth, the recommendation subjects included the controlling relationship, the upstream and downstream relationship of the supply chain, and loosely related subjects, which reflected the validity and novelty of the recommendation results. This research provides a decision-making method for effective docking between subjects.
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Received: 27 February 2019
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