Most of the time when we listen to music on the radio or on our portable devices, the order in
which the tracks are played is governed by so-called playlists. These playlists are basically …

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 …

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 …

We present the LFM-1b dataset of more than one billion music listening events created by
more than 120,000 users of Last. fm. Each listening event is characterized by artist, album …

This chapter gives an introduction to music recommender systems research. We highlight
the distinctive characteristics of music, as compared to other kinds of media. We then …

It has been widely recognized that platforms utilize their editorial capacity to transform the
industries they intermediate. In this paper, we examine the intermediary role of the leading …

We present the LFM-2b dataset containing the listening records of over 120,000 users of the
music platform Last. fm. These users provide a total of more than two billion individual …

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 …

At the core of many important machine learning problems faced by online streaming
services is a need to model how users interact with the content they are served …

Recommender systems, like other tools that make use of machine learning, are known to
create or increase certain biases. Earlier work has already unveiled different performance of …