21 citations · 75 across the 13 of their papers we have counts for
16 papers
Perceived Femininity in Singing Voice: Analysis and Prediction
Yuexuan Kong, Viet-Anh Tran, Romain Hennequin
This paper focuses on the often-overlooked aspect of perceived voice femininity in singing voices. While existing research has examined perceived voice femininity in speech, the sa…
"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…
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…
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra +1
Music streaming services often leverage sequential recommender systems to predict the best music to showcase to users based on past sequences of listening sessions. Nonetheless, mo…
Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect
Bruno Sguerra, Viet-Anh Tran, Romain Hennequin
The traditional recommendation framework seeks to connect user and content, by finding the best match possible based on users past interaction. However, a good content recommendati…
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…
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…
Personalized breath based biometric authentication with wearable multimodality
Manh-Ha Bui, Viet-Anh Tran, Cuong Pham
Breath with nose sound features has been shown as a potential biometric in personal identification and verification. In this paper, we show that information that comes from other m…
Improving Transformers with Probabilistic Attention Keys
Tam Nguyen, Tan M. Nguyen, Dung D. Le +5
Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and compute…
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…
Improving Collaborative Metric Learning with Efficient Negative Sampling
Viet-Anh Tran, Romain Hennequin, Jimena Royo-Letelier +1
Distance metric learning based on triplet loss has been applied with success in a wide range of applications such as face recognition, image retrieval, speaker change detection and…
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…
Disambiguating Music Artists at Scale with Audio Metric Learning
Jimena Royo-Letelier, Romain Hennequin, Viet-Anh Tran +1
We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task. We explore the use of metric learning techniques to learn…