Recommender systems for science: a basic taxonomy

The ever-growing availability of research artefacts of potential interest for users calls for helpers to assist their discovery. Artefacts of interest vary for the typology, e.g. papers, datasets, software. User interests are multifaceted and evolving. This paper analyses and classifies studies on recommender systems exploited to suggest research artefacts to researchers regarding the type of algorithm, users and their representations, item typologies and their representation, and evaluation methods used to assess the effectiveness of the recommendations. This study found that most of the current scientific artefacts recommender system focused only on recommending paper to individual researchers, just a few papers focused on dataset recommendation and software recommender system is unprecedented.