Towards Explainable Scientific Venue Recommendations

TL;DR

This paper introduces an interpretable venue recommendation method using non-negative matrix factorization, achieving competitive performance with simpler models compared to complex existing systems, aiding authors in selecting suitable publication venues.

Contribution

It presents a novel, interpretable venue recommendation approach based on non-negative matrix factorization, improving transparency while maintaining competitive accuracy.

Findings

Enhanced interpretability of recommendations.

Achieved competitive recommendation performance.

Simpler learning methods outperform complex models.

Abstract

Selecting the best scientific venue (i.e., conference/journal) for the submission of a research article constitutes a multifaceted challenge. Important aspects to consider are the suitability of research topics, a venue's prestige, and the probability of acceptance. The selection problem is exacerbated through the continuous emergence of additional venues. Previously proposed approaches for supporting authors in this process rely on complex recommender systems, e.g., based on Word2Vec or TextCNN. These, however, often elude an explanation for their recommendations. In this work, we propose an unsophisticated method that advances the state-of-the-art in two aspects: First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models; Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.