Abstract
Since recommender systems play an important role in our online experience today and are involved in a wide range of decisions, multiple stakeholders are requesting explanations for the corresponding algorithmic predictions. These demands—together with the benefits of explanations (e.g., trust, efficiency, and sometimes even persuasion)—have triggered significant interest from researchers in academia and in industry. Nonetheless, to the best of our knowledge, no comprehensive toolkit for development and evaluation of explainable recommender systems is available to the community yet. Instead, researchers are frequently faced with the challenge of re-implementing prior algorithms when creating and evaluating new approaches. This paper introduces recoXplainer, an easy-to-use, unified and extendable library that supports the development and evaluation of explainable recommender systems. recoXplainer includes several state-of-the-art black box algorithms, model-based and post-hoc explainability techniques, as well as offline evaluation metrics in order to assess the quality of the explanation algorithms.