Docear's architecture and datasets ease the process of designing one's own system, estimating the required development times, determining the required hardware resources to run the system, and crawling full-text papers to use as recommendation candidates. Second, we want to support researchers when building their own research paper recommender systems. This paper gives the information on Docear's recommender system that is necessary to allow the re-implementation of our approaches and to reproduce our findings. By publishing the recommender system's architecture and datasets, we pursue three goals.įirst, we want researchers to be able to understand, validate, and reproduce our research on Docear's recommender system : In our previous papers, we could often not go into detail of the recommender system due to spacial restrictions. In addition, we present four datasets containing information about a large corpus of research articles, and Docear's users, their mind-maps, and the recommendations they received. In this paper, we present the architecture of Docear's research paper recommender system. Datasets are available in several recommendation domains, including movies, music, and baby names.
Datasets empower the evaluation of recommender systems by enabling that researchers evaluate their systems with the same data. 1 Architectures help with the understanding and building of recommender systems, and are available in various recommendation domains such as e-commerce, marketing, and engineering. Researchers and developers in the field of recommender systems can benefit from publicly available architectures and datasets. Keywords: Dataset, Recommender System, Mind-map, Reference Manager, Framework, Architecture The datasets are a unique source of information to enable, for instance, research on collaborative filtering, content-based filtering, and the use of reference-management and mind-mapping software. The four datasets contain metadata of 9.4 million academic articles, including 1.8 million articles publicly available on the Web the articles' citation network anonymized information on 8,059 Docear users information about the users' 52,202 mind-maps and personal libraries and details on the 308,146 recommendations that the recommender system delivered. It supports researchers and developers in building their own research paper recommender systems, and is, to the best of our knowledge, the most comprehensive architecture that has been released in this field. for crawling PDFs, generating user models, and calculating content-based recommendations. The architecture comprises of multiple components, e.g. In this paper, we introduce the architecture of the recommender system and four datasets.
CITATION DOCEAR SOFTWARE
In the past few years, we have developed a research paper recommender system for our reference management software Docear.
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