  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.
