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 collection-specific 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.
CITATION STYLE
Risch, J., & Krestel, R. (2017). What should i cite? cross-collection reference recommendation of patents and papers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10450 LNCS, pp. 40–46). Springer Verlag. https://doi.org/10.1007/978-3-319-67008-9_4
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