Weighted meta paths and networking embedding for patent technology trade recommendations among subjects

Abstract

Most patent technology recommendations are based on link prediction of a homogeneous trade network and multiple-attribute matching. We constructed a heterogeneous information network (HIN) with four types of nodes and seven types of relations; designed a heterogeneous relation traversal algorithm based on the meta paths and meta structures inspired by the depth first search (DFS) strategy; obtained subject-relation sequences; and then calculated the weight of each meta path and meta structure through logistic regression. Using the relation sequence corpus of the weighted meta paths and meta structures among subjects, the patent technology trade recommendation model based on network embedding (PSR-vec) was proposed. The model was trained by using the Skip-gram method to obtain a vector-space representation for all subjects. Finally, the recommendation target was achieved by measuring the cosine similarity of the subject vectors. Through empirical research on the electronic information patent data, we observed that the PSR-vec model with weighted meta paths and meta structures was more precise than that with a single meta path or meta structure, which indicated that the patent technology trade was influenced by multiple factors. Second, the PSR-vec model combining weighted meta paths and meta structures was more precise than the unweighted model, which reflected more differences in multiple factors affecting trade. Third, compared to Deep Walk, Node2vec, Metapath2vec, and GraphSAGE methods, the PSR-vec model had a higher precision of up to 80%. Eventually, the recommendation subjects of PSR-vec included the holding relation, the supply relation, and the loose relation, which increased the diversity of the recommendation results. Our research thus provided a decision-making method for effective docking among patent technology trade subjects.