Leveraging online behaviors for interpretable knowledge-aware patent recommendation

Abstract

PurposePatent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.Design/methodology/approachFirst, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.FindingsExperiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.Originality/valueA meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.

Guest editorial: Interpretable AI-enabled online behavior analytics

“…It first creates a patent knowledge graph and then leverages paths in the patent knowledge graph to achieve recommendation interpretability. The proposed model achieves good performance and transparency (Du et al , 2022).…”

Research on Improvement of the Click-Through Rate Prediction Model Based on Differential Privacy

“…Hou et al [27] adopted the parallel structure of a CNN and Bi-LSTM with a self-attention mechanism for dataset entity mining, which has good cross-domain learning and recognition capabilities. Du et al [28] used an attention-based Bi-LSTM to model the sequential dependencies of entities and relationships in each connection path, ultimately generating recommendation results and explanations.…”