Simplifying Impact Prediction for Scientific Articles

Understand

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.