Simplifying Impact Prediction for Scientific Articles
This work provides a simplified and more accessible method for predicting the impact of scientific articles, which could benefit researchers and platforms in areas like article recommendation, especially when rich metadata is unavailable.
This paper addresses the problem of predicting the impact of scientific articles by reframing it as a classification task rather than a regression problem. The authors propose a simplified model that can be trained with minimal article metadata, which is shown to be effective for classifying articles based on their expected impact.
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.