Purpose

Patent 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/approach

First, 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.

Findings

Experiments 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/value

A 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.

中文翻译：

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利用在线行为进行可解释的知识感知专利推荐

目的

由于专利交易风险和专利的技术复杂性，专利交易推荐除了推荐准确性外，还需要推荐可解释性。本研究为专利交易设计了一个可解释的知识感知专利推荐模型（IKPRM）。IKPRM 首先为专利交易推荐创建专利知识图 (PKG)，然后利用 PKG 中的路径来实现推荐的可解释性。

设计/方法/方法

首先，我们构建了一个 PKG，使用自然语言处理技术整合在线公司行为和专利信息。其次，使用双向长短期记忆网络（BiLSTM）和注意力机制来建立公司的连接路径——PKG中的专利对。最后，通过为它们的连接路径分配不同的权重来计算公司——专利对的预测分数。连接路径中的语义关系有助于解释为什么推荐候选专利。

发现

在专利交易平台的真实数据集上进行的实验证实，IKPRM 在命中率和归一化折现累积增益 (nDCG) 方面明显优于基线方法。对在线用户研究的分析验证了我们建议的可解释性。

原创性/价值

基于元路径的推荐可以实现一定的可解释性，但在对异构信息进行推理时灵活性较低。为了弥合这一差距，我们提出了 IKPRM 来解释知识图中的完整路径。IKPRM 表现出良好的性能和透明度，是将可解释的人工智能集成到智能推荐等复杂任务中的坚实基础。