We provide a survey of the field of Music Information Retrieval (MIR), in particular paying
attention to latest developments, such as semantic auto-tagging and user-centric retrieval …

The music domain is among the most important ones for adopting recommender systems
technology. In contrast to most other recommendation domains, which predominantly rely on …

Profiting from large-scale training datasets, advances in neural architecture design and
efficient inference, joint embeddings have become the dominant approach for tackling cross …

This paper discusses the effect of hubness in zero-shot learning, when ridge regression is
used to find a mapping between the example space to the label space. Contrary to the …

Memory-based collaborative filtering is one of the recommendation system methods used to
predict a user's rating or preference by exploring historic ratings, but without incorporating …

We argue that an evaluation of system behavior at the level of the music is required to
usefully address the fundamental problems of music genre recognition (MGR), and indeed …

High-dimensional data arise naturally in many domains, and have regularly presented a
great challenge for traditional data mining techniques, both in terms of effectiveness and …

The scores of distance-based outlier detection methods are difficult to interpret, and it is
challenging to determine a suitable cut-off threshold between normal and outlier data points …

Relevance Popularity-based approaches are widely adopted in music recommendation
systems, both in industry and research. These approaches recommend to the target user …

In this work, we present a post-processing solution to address the hubness problem in cross-
modal retrieval, a phenomenon where a small number of gallery data points are frequently …