What should I cite?

cross-collection reference recommendation of patents and papers

Julian Risch

Ralf Krestel

pp. 40-46

in: Jaap Kamps, Giannis Tsakonas, Yannis Manolopoulos, Lazaros Iliadis, Ioannis Karydis (eds), Research and advanced technology for digital libraries, Berlin, Springer, 2017

Abstract

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.