论文标题
基于深度学习的专利引文建议系统
Deep learning-based citation recommendation system for patents
论文作者
论文摘要
在这项研究中，我们解决了开发基于深度学习的自动专利引文推荐系统的挑战。尽管基于深度学习的推荐系统在各个领域（例如电影，产品和纸张引用）中表现出了出色的性能，但由于缺乏免费的高质量数据集和相关基准模型，因此尚未研究其在专利引用中的有效性。为了解决这些问题，我们提供了一个名为Patennet的新颖数据集，其中包括文本信息和元数据，用于Google Big查询服务的大约110,000个专利。此外，我们建议考虑文本信息和元数据的相似性（例如合作专利分类代码）的强大基准模型。与现有的建议方法相比，所提出的基准方法在测试集上达到了平均相互等级为0.2377，而现有的最新建议方法达到了0.2073。
In this study, we address the challenges in developing a deep learning-based automatic patent citation recommendation system. Although deep learning-based recommendation systems have exhibited outstanding performance in various domains (such as movies, products, and paper citations), their validity in patent citations has not been investigated, owing to the lack of a freely available high-quality dataset and relevant benchmark model. To solve these problems, we present a novel dataset called PatentNet that includes textual information and metadata for approximately 110,000 patents from the Google Big Query service. Further, we propose strong benchmark models considering the similarity of textual information and metadata (such as cooperative patent classification code). Compared with existing recommendation methods, the proposed benchmark method achieved a mean reciprocal rank of 0.2377 on the test set, whereas the existing state-of-the-art recommendation method achieved 0.2073.
