论文标题
简化科学文章的影响预测
Simplifying Impact Prediction for Scientific Articles
论文作者
论文摘要
估计文章的预期影响对于各种应用程序（例如文章/合作者建议）很有价值。大多数现有的方法试图预测每篇文章将在不久的将来收到的准确引用数量，但这是一个困难的回归分析问题。此外，大多数方法都依赖于每篇文章的丰富元数据的存在，这一要求无法充分满足其中的大量要求。在这项工作中，我们利用了一个事实，即解决一个简单的机器学习问题，基于其预期影响的文章进行分类，这对于许多现实世界应用就足够了，我们提出了一个可以使用最小文章元数据进行培训的简化模型。最后，我们检查了该模型的各种配置，并评估了它们在解决上述分类问题方面的有效性。
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.
