Alessandro B. Melchiorre

Deepak Kumar, Shahed Masoudian, Alessandro B. Melchiorre, Markus Schedl. Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI). 2025.

Music listening has experienced a sharp increase during the last decade thanks to music streaming and recommendation services. While they offer text-based search functionality and provide recommendation lists of remarkable utility, their typical mode of interaction is unidimensional, i.e., they provide lists of consecutive tracks, which are commonly inspected in sequential order by the user. The user experience with such systems is heavily affected by cognition biases (e.g., position bias, human tendency to pay more attention to first positions of ordered lists) as well as algorithmic biases (e.g., popularity bias, the tendency of recommender systems to overrepresent popular items). This may cause dissatisfaction among the users by disabling them to find novel music to enjoy. In light of such systems and biases, we propose an intelligent audiovisual music exploration system namedEmoMTB . It allows the user to browse the entirety of a given collection in a free nonlinear fashion. The navigation is assisted by a set of personalized emotion-aware recommendations, which serve as starting points for the exploration experience.EmoMTB adopts the metaphor of a city, in which each track (visualized as a colored cube) represents one floor of a building. Highly similar tracks are located in the same building; moderately similar ones form neighborhoods that mostly correspond to genres. Tracks situated between distinct neighborhoods create a gradual transition between genres. Users can navigate this music city using their smartphones as control devices. They can explore districts of well-known music or decide to leave their comfort zone. In addition,EmoMTB integrates an emotion-aware music recommendation system that re-ranks the list of suggested starting points for exploration according to the user’s self-identified emotion or the collective emotion expressed inEmoMTB ’s Twitter channel. Evaluation ofEmoMTB has been carried out in a threefold way: by quantifying the homogeneity of the clustering underlying the construction of the city, by measuring the accuracy of the emotion predictor, and by carrying out a web-based survey composed of open questions to obtain qualitative feedback from users.

Recent studies show the benefits of reformulating common machine learning models through the concept of prototypes – representatives of the underlying data, used to calculate the prediction score as a linear combination of similarities of a data point to prototypes. Such prototype-based formulation of a model, in addition to preserving (sometimes enhancing) the performance, enables explainability of the model’s decisions, as the prediction can be linearly broken down into the contributions of distinct definable prototypes. Following this direction, we extend the idea of prototypes to the recommender system domain by introducing ProtoMF, a novel collaborative filtering algorithm. ProtoMF learns sets of user/item prototypes that represent the general consumption characteristics of users/items in the underlying dataset. Using these prototypes, ProtoMF then represents users and items as vectors of similarities to the corresponding prototypes. These user/item representations are ultimately leveraged to make recommendations that are both effective in terms of accuracy metrics, and explainable through the interpretation of prototypes’ contributions to the affinity scores. We conduct experiments on three datasets to assess both the effectiveness and the explainability of ProtoMF. Addressing the former, we show that ProtoMF exhibits higher Hit Ratio and NDCG compared to other relevant collaborative filtering approaches. As for the latter, we qualitatively show how ProtoMF can provide explainable recommendations and how its explanation capabilities can expose the existence of statistical biases in the learned representations, which we exemplify for the case of gender bias.

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 MRS and provide research perspectives.