71 citations · 171 across the 19 of their papers we have counts for
7 papers · 1 filter
Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation
Elena V. Epure, Yashar Deldjoo, Bruno Sguerra +2
Music Recommender Systems (MRSs) have long relied on an information retrieval framing, where progress is measured mainly through accuracy on retrieval-oriented subtasks. While effe…
Learning Linearity in Audio Consistency Autoencoders via Implicit Regularization
Bernardo Torres, Manuel Moussallam, Gabriel Meseguer-Brocal
Audio autoencoders learn useful, compressed audio representations, but their non-linear latent spaces prevent intuitive algebraic manipulation such as mixing or scaling. We introdu…
The dynamics of discovery and the Heaps-Zipf relationship
Célestin Zimmerlin, Thomas Louail, Manuel Moussallam +1
When following a sequence - such as reading a text or tracking a user's activity - one can measure how the "dictionary" of distinct elements (types) grows with the number of observ…
"Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation
Viet-Anh Tran, Bruno Sguerra, Gabriel Meseguer-Brocal +2
On music streaming services, listening sessions are often composed of a balance of familiar and new tracks. Recently, sequential recommender systems have adopted cognitive-informed…
Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation
Alessandro B. Melchiorre, Elena V. Epure, Shahed Masoudian +4
Natural language interfaces offer a compelling approach for music recommendation, enabling users to express complex preferences conversationally. While Large Language Models (LLMs)…
Modeling Musical Genre Trajectories through Pathlet Learning
Lilian Marey, Charlotte Laclau, Bruno Sguerra +2
The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user prefe…
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Bruno Sguerra, Viet-Anh Tran, Romain Hennequin +1
Recommender systems rely heavily on user feedback to learn effective user and item representations. Despite their widespread adoption, limited attention has been given to the uncer…