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What should I cite?

cross-collection reference recommendation of patents and papers

Julian Risch, Ralf Krestel

pp. 40-46

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.

Publication details

DOI: 10.1007/978-3-319-67008-9_4

Full citation:

Risch, J. , Krestel, R. (2017)., What should I cite?: cross-collection reference recommendation of patents and papers, in J. Kamps, G. Tsakonas, Y. Manolopoulos, L. Iliadis & I. Karydis (eds.), Research and advanced technology for digital libraries, Dordrecht, Springer, pp. 40-46.

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