What Should I Cite? Cross-Collection Reference Recommendation of Patents and Papers

被引:4
|
作者
Risch, Julian [1 ]
Krestel, Ralf [1 ]
机构
[1] Hasso Plattner Inst, Prof Dr Helmert Str 2-3, D-14482 Potsdam, Germany
来源
RESEARCH AND ADVANCED TECHNOLOGY FOR DIGITAL LIBRARIES (TPDL 2017) | 2017年 / 10450卷
关键词
Recommender systems; Text mining; Topic modeling;
D O I
10.1007/978-3-319-67008-9_4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Research results manifest in large corpora of patents and scientific papers. However, both corpora lack a consistent taxonomy and references across different document types are sparse. Therefore, and because of contrastive, domain-specific language, recommending similar papers for a given patent (or vice versa) is challenging. We propose a recommender system that leverages topic distributions and keywords to recommend related work despite these challenges. As a case study, we evaluate our approach on patents and papers of two fields: medical and computer science. We find that topic-based recommenders complement word-based recommenders for documents with collectionspecific language and increase mean average precision by up to 27%. As a result of our work, publications from both corpora form a joint digital library, which connects academia and industry.
引用
收藏
页码:40 / 46
页数:7
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