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 design of recommendation algorithms aware of the user's context has been the subject
of great interest in the scientific community, especially in the music domain where contextual …

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 …

Music can produce a positive effect in runners' motivation and performance. Nevertheless,
these effects vary depending on the user's location, the emotions that she/he feels at each …

New distribution channels like music streaming platforms paved way for making more and
more diverse music available to users. Thus, music recommender systems got in the focus of …

Recent advances in deep learning have yielded new approaches for music
recommendation in the long tail. The new approaches are based on data related to the …

The fast evolution of e-learning systems offers learners with huge opportunities for
accessing learning activities via online. This largely provides support and improvement to …

Music listening is an inherently cultural behavior, which may be shaped by users'
backgrounds and contextual characteristics. Due to geographical, socio-economic …

Hashtags are keywords describing a topic or a theme and are usually chosen by
microblogging users. Hence, the hashtags can be used to categorize microblog posts. With …

Integrating information about the listener's cultural background when building music
recommender systems has recently been identified as a means to improve recommendation …