An explainable content-based approach for recommender systems: a case study in journal recommendation for paper submission

Abstract

Explainable artificial intelligence is becoming increasingly important in new artificial intelligence developments since it enables users to understand and consequently trust system output. In the field of recommender systems, explanation is necessary not only for such understanding and trust but also because if users understand why the system is making certain suggestions, they are more likely to consume the recommended product. This paper proposes a novel approach for explaining content-based recommender systems by specifically focusing on publication venue recommendation. In this problem, the authors of a new research paper receive recommendations about possible journals (or other publication venues) to which they could submit their article based on content similarity, while the recommender system simultaneously explains its decisions. The proposed explanation ecosystem is based on various elements that support the explanation (topics, related articles, relevant terms, etc.) and is fully integrated with the underlying recommendation model. The proposed method is evaluated through a user study in the biomedical field, where transparency, satisfaction, trust, and scrutability are assessed. The obtained results suggest that the proposed approach is effective and useful for explaining the output of the recommender system to users.