With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company’s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company’s historical records. In addition, to address a company’s diverse technology interests, we design an attention mechanism to aggregate the company’s historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.

随着专利市场的快速发展，需要专利交易推荐来降低专利购买者的技术搜索成本。目前的研究侧重于基于公司现有专利的推荐；一些研究考虑了专利获取活动的顺序模式和公司商业利益的可能多样性。此外，仅基于专利文件的专利分析无法捕捉专利的高阶信息。为了弥补这一差距，我们提出了一种用于专利交易推荐的知识感知注意双向长短期记忆网络（KBiLSTM）方法。KBiLSTM 使用知识图嵌入来描述具有丰富专利信息的专利。它引入了双向长短期记忆网络 (BiLSTM) 来捕获公司历史记录中的序列模式。此外，为了满足公司多样化的技术利益，我们设计了一种注意力机制，以汇总公司给定候选专利的历史专利。美国专利商标局 (USPTO) 数据集的实验结果表明，KBiLSTM 在 F1 和归一化贴现累积增益 (nDCG) 方面优于最先进的专利交易推荐基线。随机选择公司的注意力可视化直观地展示了推荐效果。我们设计了一个注意力机制来聚合给定候选专利的公司历史专利。美国专利商标局 (USPTO) 数据集的实验结果表明，KBiLSTM 在 F1 和归一化贴现累积增益 (nDCG) 方面优于最先进的专利交易推荐基线。随机选择公司的注意力可视化直观地展示了推荐效果。我们设计了一个注意力机制来聚合给定候选专利的公司历史专利。美国专利商标局 (USPTO) 数据集的实验结果表明，KBiLSTM 在 F1 和归一化贴现累积增益 (nDCG) 方面优于最先进的专利交易推荐基线。随机选择公司的注意力可视化直观地展示了推荐效果。