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Selecting the best scientific venue (i.e., conference/journal) for thesubmission of a research article constitutes a multifaceted challenge.Important aspects to consider are the suitability of research topics, a venue'sprestige, and the probability of acceptance. The selection problem isexacerbated through the continuous emergence of additional venues. Previouslyproposed approaches for supporting authors in this process rely on complexrecommender systems, e.g., based on Word2Vec or TextCNN. These, however, oftenelude an explanation for their recommendations. In this work, we propose anunsophisticated method that advances the state-of-the-art in two aspects:First, we enhance the interpretability of recommendations through non-negativematrix factorization based topic models; Second, we surprisingly can obtaincompetitive recommendation performance while using simpler learning methods.

Towards Explainable Scientific Venue Recommendations