Affiliations: [a]
Eötvös Loránd University, Faculty of Informatics, Budapest, Hungary
| [b]
Inst. Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), Budapest, Hungary
| [c] Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, France
| [d]
Department of Computer Science, Aalto University, Espoo, Finland
Correspondence:
[*]
Corresponding author. Frederick Ayala-Gómez, Eötvös Loránd University, Faculty of Informatics, 1117 Budapest, Hungary. [email protected]
Abstract: Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs – and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.
Keywords: Citation recommendations, knowledge graphs, recommender systems
