Recommender systems for science: a basic taxonomy

This dataset is accompanying the "Recommender system for science: A basic taxonomy" paper published at IRCDL 2022 conference. This study had a Systematic Mapping Approach on the Recommender system for science. In particular, the study aims at responding to four questions on recommender systems in science cases: users and their interests representation, item typologies and their representation, recommendation algorithms, and evaluation, and then providing a taxonomy. This dataset contains 209 papers of interest that have been published between 2015 and 2022. The dataset has 11 columns which organised as follows: Column Title: This column contains the title of the papers. Column DOI: This column contains the DOI of the papers. Column Publication_year: This column contains the year that the paper is published. Column DB: This column contains the repository that the paper is retrieved. Column Keywords: This column contains the keywords provided for the paper. Column Content_type: This column contains the paper type which can be: Article, Conference or Review. Column Citing_paper_count: This column contains the citation number of the paper. Column Recommended_artefact: This column contains the scientific product that is recommended to users which can be paper, workflow, collaborator, dataset or others. Column User_type: This column contains the type of user who receives the recommendation, which can be an Individual user or a Group of users. Column Algorithm: This column contains the recommendation algorithm that the paper proposed, which can be: HB (Hybrid-based), CB (Content-based), CFB (Collaborative-filtering-based), or GB (Graph-based). Column Evaluation_method: This column contains the method of the algorithm evaluation which can be OFFLINE, ONLINE, BOTH, or NO_EVALUATION.