Music tagging and content-based retrieval systems have traditionally been constructed
using pre-defined ontologies covering a rigid set of music attributes or text queries. This …

Although recommender systems (RSs) play a crucial role in our society, previous studies
have revealed that the performance of RSs may considerably differ between groups of …

Personalized recommendation serves as a ubiquitous channel for users to discover
information tailored to their interests. However, traditional recommendation models primarily …

We demonstrate that language models pre-trained on codified (discretely-encoded) music
audio learn representations that are useful for downstream MIR tasks. Specifically, we …

This chapter gives an introduction to music recommender systems, considering the unique
characteristics of the music domain. We take a user-centric perspective, by organizing our …

In this work, we provide a broad comparative analysis of strategies for pre-training audio
understanding models for several tasks in the music domain, including labelling of genre …

Generative models are a class of AI models capable of creating new instances of data by
learning and sampling from their statistical distributions. In recent years, these models have …

Audio representations for music information retrieval are typically learned via supervised
learning in a task-specific fashion. Although effective at producing state-of-the-art results …

Personalized recommendation on new track releases has always been a challenging
problem in the music industry. To combat this problem, we first explore user listening history …

As online music consumption increasingly shifts towards playlist-based listening, the task of
playlist continuation, in which an algorithm suggests songs to extend a playlist in a …