%0 Generic %1 schafermeier2021towards %A Schäfermeier, Bastian %A Stumme, Gerd %A Hanika, Tom %D 2021 %K myown venue_recommendations %T Towards Explainable Scientific Venue Recommendations %U http://arxiv.org/abs/2109.11343 %X 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.

@misc{schafermeier2021towards, 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.}, added-at = {2023-01-17T13:03:39.000+0100}, author = {Schäfermeier, Bastian and Stumme, Gerd and Hanika, Tom}, biburl = {https://www.bibsonomy.org/bibtex/279a9e82152e6416811ddb2b2f6887b75/kde-alumni}, description = {Towards Explainable Scientific Venue Recommendations}, interhash = {6388a7a1875359f629c79bf31db5cb8a}, intrahash = {79a9e82152e6416811ddb2b2f6887b75}, keywords = {myown venue_recommendations}, note = {cite arxiv:2109.11343}, timestamp = {2023-01-17T13:59:48.000+0100}, title = {Towards Explainable Scientific Venue Recommendations}, url = {http://arxiv.org/abs/2109.11343}, year = 2021 }