35 citations · 94 across the 13 of their papers we have counts for
20 papers
On the Consistency of Average Embeddings for Item Recommendation
Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin +2
A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates t…
Track Mix Generation on Music Streaming Services using Transformers
Walid Bendada, Théo Bontempelli, Mathieu Morlon +4
This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists…
A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
Walid Bendada, Guillaume Salha-Galvan, Thomas Bouabça +1
Music streaming services often aim to recommend songs for users to extend the playlists they have created on these services. However, extending playlists while preserving their mus…
Attention Mixtures for Time-Aware Sequential Recommendation
Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra +1
Transformers emerged as powerful methods for sequential recommendation. However, existing architectures often overlook the complex dependencies between user preferences and the tem…
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…
Flow Moods: Recommending Music by Moods on Deezer
Théo Bontempelli, Benjamin Chapus, François Rigaud +3
The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. No…
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation
Guillaume Salha-Galvan
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as two powerful groups of unsupervised node embedding methods, with various applications to graph-based m…
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…
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
Guillaume Salha-Galvan, Romain Hennequin, Benjamin Chapus +2
On an artist's profile page, music streaming services frequently recommend a ranked list of "similar artists" that fans also liked. However, implementing such a feature is challeng…
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…
A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps
Léa Briand, Guillaume Salha-Galvan, Walid Bendada +2
Music streaming services heavily rely on recommender systems to improve their users' experience, by helping them navigate through a large musical catalog and discover new songs, al…
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…
Carousel Personalization in Music Streaming Apps with Contextual Bandits
Walid Bendada, Guillaume Salha, Théo Bontempelli
Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users. However, selecting the most r…
Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation
Elena V. Epure, Guillaume Salha, Romain Hennequin
Annotating music items with music genres is crucial for music recommendation and information retrieval, yet challenging given that music genres are subjective concepts. Recently, i…
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…
Simple and Effective Graph Autoencoders with One-Hop Linear Models
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks suc…
Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks
Guillaume Salha, Romain Hennequin, Michalis Vazirgiannis
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link predic…
Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Guillaume Salha, Stratis Limnios, Romain Hennequin +2
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackl…
A Degeneracy Framework for Scalable Graph Autoencoders
Guillaume Salha, Romain Hennequin, Viet Anh Tran +1
In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train…
Adaptive Submodular Influence Maximization with Myopic Feedback
Guillaume Salha, Nikolaos Tziortziotis, Michalis Vazirgiannis
This paper examines the problem of adaptive influence maximization in social networks. As adaptive decision making is a time-critical task, a realistic feedback model has been cons…