Towards Explainable Scientific Venue Recommendations
This work addresses the challenge for researchers needing explainable venue recommendations, but it is incremental as it builds on existing recommender systems.
The authors tackled the problem of selecting scientific venues for article submission by proposing a method that improves interpretability through non-negative matrix factorization topic models and achieves competitive performance with simpler learning methods.
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.