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.
翻译:监听当今音乐记录的最常见方法是通过流流式平台,提供数千万条音轨的接入。为了协助用户有效浏览这些大型目录,整合音乐建议系统变得至关重要。当前现实世界的MSS通常相当复杂,并且最优化地符合建议准确性。它们结合了基于协作过滤和内容建议的若干构件。这种复杂性会妨碍向终端用户解释建议的能力,对于被认为出乎意料或不适当的建议来说尤其重要。纯建议性业绩往往与用户满意度相关,但解释性对信任和宽恕等其他要素具有积极影响,而信任和宽恕最终对于保持用户忠诚至关重要。在本篇文章中,我们讨论如何在MRS背景下解决解释性的问题。我们提供了关于解释性的观点,说明如何改进音乐建议算法和基于内容的建议。首先,我们审查建议者解释性的共同层面和目标,以及一般而言,基于电子的人工智能信息(XAI),并详细说明这些应用的范围 -- 或需要加以调整 -- -- 这些因素对信任和宽恕性等其他因素产生了积极影响,而这些因素最终对于保持用户忠诚性至关重要。在本文章中,我们讨论如何在M- 进行精确性解释的准确性研究中解释,然后,我们又能够说明我们如何在对当前消费和建议的准确性评估中作出何种解释。