Explainability in Music Recommender Systems

Abstract

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.

Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge

“…PLAYNTELL leverages general linguistic knowledge from a pre-trained language model to generate coherent captions, and musical knowledge to make the captions consistent with the playlist content. Planned future work include the extension to non-English captions, and to apply PLAYN-TELL in the field of music recommender systems, where captions could explain automatically generated playlists (Afchar et al, 2022).…”

“…However, describing playlists, especially the ones created by recommendation algorithms, is very labor-intensive and time-consuming when done manually (Doh et al, 2021). When automatic captions are proposed, these often rely on pre-defined templates thus cannot cover all kinds of cases, similar to tags (Afchar et al, 2022).…”

“…To address the above limitations, Choi et al (2016) introduce the task of playlist captioning, that is automatically describing a playlist using natural language. Playlist captioning can enable several useful applications, such as assisting curators in the process of finding an appropriate caption for a playlist; enabling search and discovery of playlists through human-like queries (Manco et al, 2021); assigning captions to algorithm-generated playlists, that could be also used as explanations for automatic playlist recommendations (Afchar et al, 2022). Even so, playlist captioning is still an under-researched topic.…”