Building and Deploying the Digital Humanities Quarterly Recommender System

Since 2007, Digital Humanities Quarterly has published over 750 scholarly articles, constituting a significant repository of scholarship within the digital humanities. As the journal’s corpus of articles continues to grow, it is no longer possible for readers to manually navigate the title and abstract of every article in order to stay apprised of relevant work or conduct literature reviews. To address this, we have implemented a recommender system for the Digital Humanities Quarterly corpus, generating recommendations of related articles that appear below each article on the journal’s website with the goal of improving discoverability. These recommendations are generated via three different methods: a keyword-based approach based on a controlled vocabulary of topics assigned to articles by editors; a TF-IDF approach applied to full article text; and a deep learning approach using the Allen Institute for Artificial Intelligence’s SPECTER2 model applied to article titles and abstracts. In this article, we detail our process of creating this recommender system, from the article pre-processing pipeline to the front-end implementation of the recommendations on the Digital Humanities Quarterly website.