A survey on popularity bias in recommender systems
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 …
promise of such systems is that they are able to increase the visibility of items in the long tail …
Context-aware recommender systems in the music domain: A systematic literature review
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 …
of great interest in the scientific community, especially in the music domain where contextual …
What is fair? Exploring the artists' perspective on the fairness of music streaming platforms
Music streaming platforms are currently among the main sources of music consumption, and
the embedded recommender systems significantly influence what the users consume. There …
the embedded recommender systems significantly influence what the users consume. There …
Feature-combination hybrid recommender systems for automated music playlist continuation
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 …
users with the increasingly larger music catalogs of on-line music streaming services, on …
[PDF][PDF] A survey on popularity bias in recommender systems
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 …
promise of such systems is that they are able to increase the visibility of items in the long tail …
A comparative analysis of bias amplification in graph neural network approaches for recommender systems
Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …
recommendations and help them overcome the problem of information overload. Currently …
Listener modeling and context-aware music recommendation based on country archetypes
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 …
the listener, which is reflected, to a considerable extent, in country-specific music listening …
Music cold-start and long-tail recommendation: bias in deep representations
A Ferraro - Proceedings of the 13th ACM Conference on …, 2019 - dl.acm.org
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 …
recommendation in the long tail. The new approaches are based on data related to the …
[PDF][PDF] Modeling Popularity and Temporal Drift of Music Genre Preferences.
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 …
preferences of users. We introduce a novel user modeling approach, BLLu, which takes into …
Surveying More Than Two Decades of Music Information Retrieval Research on Playlists
In this article, we present an extensive survey of music information retrieval (MIR) research
into music playlists. Our survey spans more than 20 years, and includes around 300 papers …
into music playlists. Our survey spans more than 20 years, and includes around 300 papers …