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