  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.
