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20182026
most citedExplainability in Music Recommender Systems

71 citations · 248 across the 43 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.LG2022

New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction

Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2

Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detecti…

cs.HC2022★ 13 cited

Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization

Bruno Sguerra, Viet-Anh Tran, Romain Hennequin

Repetition in music consumption is a common phenomenon. It is notably more frequent when compared to the consumption of other media, such as books and movies. In this paper, we sho…

cs.SD2022★ 1 cited

Learning Unsupervised Hierarchies of Audio Concepts

Darius Afchar, Romain Hennequin, Vincent Guigue

Music signals are difficult to interpret from their low-level features, perhaps even more than images: e.g. highlighting part of a spectrogram or an image is often insufficient to…

cs.LG2022★ 1 cited

Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction

Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2

Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection pr…

cs.LG2022★ 71 cited

Explainability in Music Recommender Systems

Darius Afchar, Alessandro B. Melchiorre, Markus Schedl +3

The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing thes…