Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …

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 …

Music streaming platforms are currently among the main sources of music consumption, and
the embedded recommender systems significantly influence what the users consume. There …

Music recommender systems have become a key technology to support the interaction of
users with the increasingly larger music catalogs of on-line music streaming services, on …

Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …

Music preferences are strongly shaped by the cultural and socio-economic background of
the listener, which is reflected, to a considerable extent, in country-specific music listening …

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 …

In this paper, we address the problem of modeling and predicting the music genre
preferences of users. We introduce a novel user modeling approach, BLLu, which takes into …

Joint caching and recommendation has been recently proposed as a new paradigm for
increasing the efficiency of mobile edge caching. Early findings demonstrate significant …

The act of telling stories is a fundamental part of what it means to be human. This work
introduces the concept of narrative information, which we define as the overlap in …