#DOCEAR AS REFERENCE MANAGEMENT SOFTWARE#

only publish unique IDs of the articles and no title or author names. These 50,000 libraries contain 4.4 million articles with 3.6 million of them being unique. The dataset includes 50,000 randomly selected personal libraries from 1.5 million users.

#DOCEAR AS REFERENCE MANAGEMENT SOFTWARE#

Kris Jack, et al., compiled a dataset based on the reference management software Mendeley. CiteSeer's dataset has been frequently used by researchers for evaluating research paper recommender systems. CiteSeer made its corpus of research papers public, as well as the citation graph of the articles, data for author name disambiguation, and the co-author network. The datasets were not originally intended for recommender system research but are frequently used for this purpose. CiteULike and Bibsonomy published datasets containing the social tags that their users added to research articles. Several academic services published datasets, and hence have eased the process of researching and developing research paper recommender systems. This paper will present related work, provide a general overview of Docear and its recommender system, introduce the architecture, and present the datasets. Our datasets allow analyses beyond the analyses we have already published, for instance to evaluate collaborative filtering algorithms, perform citation analysis, or explore the use of reference managers. This is of particular importance, since the majority of researchers in the field of research paper recommender systems have no access to real-world recommender systems.

Third, we want to provide real-world data to researchers who have no access to such data. 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.

In the past few years, we have developed a research paper recommender system for our reference management software Docear.

Docear as reference management