The approaches differed by the features to utilize (terms or citations), by user model size, whether stop-words were removed, and several other factors. We implemented different content-based filtering approaches in the research-paper recommender system of Docear. ![]() offline evaluations, online evaluations, and user studies, in the context of research-paper recommender systems. In this paper, we examine and discuss the appropriateness of different evaluation methods, i.e. However, recommender-systems evaluation has received too little attention in the recommender-system community, in particular in the community of research paper recommender systems. The evaluation of recommender systems is key to the successful application of rec-ommender systems in practice. 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. ![]() In the past few years, we have developed a research paper recommender system for our reference management software Docear.
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