Related papers: Simplifying Impact Prediction for Scientific Articles

Simplifying Impact Prediction for Scientific Articles

URL: http://arxiv.org/abs/2012.15192v1
Date: Wed, 30 Dec 2020 15:24:55 GMT
Title: Simplifying Impact Prediction for Scientific Articles
Authors: Thanasis Vergoulis, Ilias Kanellos, Giorgos Giannopoulos, Theodore Dalamagas
Abstract summary: Estimating the expected impact of an article is valuable for various applications. We propose a model that can be trained using minimal article metadata.
Score: 1.8352113484137624
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Estimating the expected impact of an article is valuable for various applications (e.g., article/cooperator recommendation). Most existing approaches attempt to predict the exact number of citations each article will receive in the near future, however this is a difficult regression analysis problem. Moreover, most approaches rely on the existence of rich metadata for each article, a requirement that cannot be adequately fulfilled for a large number of them. In this work, we take advantage of the fact that solving a simpler machine learning problem, that of classifying articles based on their expected impact, is adequate for many real world applications and we propose a simplified model that can be trained using minimal article metadata. Finally, we examine various configurations of this model and evaluate their effectiveness in solving the aforementioned classification problem.

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