The existing scientific documentation-based recommender systems focus on exploiting the citations and references information included in each research paper and also the lists of co-authors. In this way, it can be addressed the recommendation of related papers and even related authors. The approach we propose is original because instead of using each paper citations and co-authors, we relate each of the papers with their main research topics. This approach provides a semantic level superior to that currently used, which allows us to obtain useful results. We can use collaborative filtering recommender systems to recommend research topics related to each paper and also to recommend papers related to each research topic. In order to face this innovative proposal, we have solved a series of challenges that allow us to offer various resources and results in the paper. Our main contributions are: 1) making a data mining of scientific documentation; 2) creating and publishing an open database containing the data mining results; 3) extracting the research topics from the available scientific documentation; 4) creating and publishing a recommender system data set obtained from the database and the research topics; 5) testing the data set through a complete set of collaborative filtering methods and quality measures; and 6) selecting and showing the best methods and results, obtained using the open data set, in the context of scientific documentation recommendations. Results of the paper show the suitability of the provided data set in collaborative filtering processes, as well as the superiority of the model-based methods to face scientific documentation recommendations.
Ortega, F., Bobadilla, J., Gutierrez, A., Hurtado, R., & Li, X. (2018). Artificial Intelligence Scientific Documentation Dataset for Recommender Systems. IEEE Access, 6, 48543–48555. https://doi.org/10.1109/ACCESS.2018.2867731