21 citations · 48 across the 4 of their papers we have counts for
10 papers
Modeling Activity-Driven Music Listening with PACE
Lilian Marey, Bruno Sguerra, Manuel Moussallam
While the topic of listening context is widely studied in the literature of music recommender systems, the integration of regular user behavior is often omitted. In this paper, we…
Uplifting Interviews in Social Science with Individual Data Visualization: the case of Music Listening
Robin Cura, Amélie Beaumont, Jean-Samuel Beuscart +6
Collecting accurate and fine-grain information about the music people like, dislike and actually listen to has long been a challenge for sociologists. As millions of people now use…
Follow the guides: disentangling human and algorithmic curation in online music consumption
Quentin Villermet, Jérémie Poiroux, Manuel Moussallam +2
The role of recommendation systems in the diversity of content consumption on platforms is a much-debated issue. The quantitative state of the art often overlooks the existence of…
Hierarchical Latent Relation Modeling for Collaborative Metric Learning
Viet-Anh Tran, Guillaume Salha-Galvan, Romain Hennequin +1
Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods lea…
Modeling the Music Genre Perception across Language-Bound Cultures
Elena V. Epure, Guillaume Salha, Manuel Moussallam +1
The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere…
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy +2
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general f…