71 citations · 294 across the 49 of their papers we have counts for
13 papers · 1 filter
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…
Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation
Elena V. Epure, Gabriel Meseguer-Brocal, Darius Afchar +1
Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categ…
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…
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…
Of Spiky SVDs and Music Recommendation
Darius Afchar, Romain Hennequin, Vincent Guigue
The truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval or embedding musical items for downstream tasks. T…
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…
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…
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…
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…
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
Rémi Delbouys, Romain Hennequin, Francesco Piccoli +2
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track. We reproduce the implementation of traditional feature engineering bas…
Audio Based Disambiguation Of Music Genre Tags
Romain Hennequin, Jimena Royo-Letelier, Manuel Moussallam
In this paper, we propose to infer music genre embeddings from audio datasets carrying semantic information about genres. We show that such embeddings can be used for disambiguatin…
Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features
Albin Andrew Correya, Romain Hennequin, Mickaël Arcos
Cover song detection is a very relevant task in Music Information Retrieval (MIR) studies and has been mainly addressed using audio-based systems. Despite its potential impact in i…