Explainability in music recommender systems

The most common way to listen recorded music nowadays is via streaming platforms which provide access tens of millions tracks. To assist users in effectively browsing these large catalogs, the integration Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering content-based recommendation. This complexity can hinder ability explain recommendations end users, particularly important perceived as unexpected or inappropriate. While pure performance correlates with user satisfaction, explainability a positive impact other factors such trust forgiveness, ultimately essential maintain loyalty. In this article, we discuss how be addressed context MRSs. We perspectives could improve algorithms enhance experience. First, review dimensions goals recommenders' general eXplainable Artificial Intelligence (XAI), elaborate extent apply -- need adapted specific characteristics consumption Then, show components integrated within MRS what form explanations provided. Since evaluation explanation quality decoupled from accuracy-based criteria, also requirements strategies evaluating recommendations. Finally, describe current challenges introducing large-scale industrial recommender system research perspectives.